The estimates of the high-dimensional volatility matrix based on high-frequency data play a pivotal role in many financial applications.However,most existing studies have been built on the sub-Gaussian and cross-secti...The estimates of the high-dimensional volatility matrix based on high-frequency data play a pivotal role in many financial applications.However,most existing studies have been built on the sub-Gaussian and cross-sectional independence assumptions of microstructure noise,which are typically violated in the financial markets.In this paper,the authors proposed a new robust volatility matrix estimator,with very mild assumptions on the cross-sectional dependence and tail behaviors of the noises,and demonstrated that it can achieve the optimal convergence rate n-1/4.Furthermore,the proposed model offered better explanatory and predictive powers by decomposing the estimator into low-rank and sparse components,using an appropriate regularization procedure.Simulation studies demonstrated that the proposed estimator outperforms its competitors under various dependence structures of microstructure noise.Additionally,an extensive analysis of the high-frequency data for stocks in the Shenzhen Stock Exchange of China demonstrated the practical effectiveness of the estimator.展开更多
Substantial effects of photochemical reaction losses of volatile organic compounds(VOCs)on factor profiles can be investigated by comparing the differences between daytime and nighttime dispersion-normalized VOC data ...Substantial effects of photochemical reaction losses of volatile organic compounds(VOCs)on factor profiles can be investigated by comparing the differences between daytime and nighttime dispersion-normalized VOC data resolved profiles.Hourly speciated VOC data measured in Shijiazhuang,China from May to September 2021 were used to conduct study.The mean VOC concentration in the daytime and at nighttime were 32.8 and 36.0 ppbv,respectively.Alkanes and aromatics concentrations in the daytime(12.9 and 3.08 ppbv)were lower than nighttime(15.5 and 3.63 ppbv),whereas that of alkenes showed the opposite tendency.The concentration differences between daytime and nighttime for alkynes and halogenated hydrocarbonswere uniformly small.The reactivities of the dominant species in factor profiles for gasoline emissions,natural gas and diesel vehicles,and liquefied petroleum gas were relatively low and their profiles were less affected by photochemical losses.Photochemical losses produced a substantial impact on the profiles of solvent use,petrochemical industry emissions,combustion sources,and biogenic emissions where the dominant species in these factor profiles had high reactivities.Although the profile of biogenic emissions was substantially affected by photochemical loss of isoprene,the low emissions at nighttime also had an important impact on its profile.Chemical losses of highly active VOC species substantially reduced their concentrations in apportioned factor profiles.This study results were consistent with the analytical results obtained through initial concentration estimation,suggesting that the initial concentration estimation could be the most effective currently availablemethod for the source analyses of active VOCs although with uncertainty.展开更多
Latent factor(LF)models are highly effective in extracting useful knowledge from High-Dimensional and Sparse(HiDS)matrices which are commonly seen in various industrial applications.An LF model usually adopts iterativ...Latent factor(LF)models are highly effective in extracting useful knowledge from High-Dimensional and Sparse(HiDS)matrices which are commonly seen in various industrial applications.An LF model usually adopts iterative optimizers,which may consume many iterations to achieve a local optima,resulting in considerable time cost.Hence,determining how to accelerate the training process for LF models has become a significant issue.To address this,this work proposes a randomized latent factor(RLF)model.It incorporates the principle of randomized learning techniques from neural networks into the LF analysis of HiDS matrices,thereby greatly alleviating computational burden.It also extends a standard learning process for randomized neural networks in context of LF analysis to make the resulting model represent an HiDS matrix correctly.Experimental results on three HiDS matrices from industrial applications demonstrate that compared with state-of-the-art LF models,RLF is able to achieve significantly higher computational efficiency and comparable prediction accuracy for missing data.I provides an important alternative approach to LF analysis of HiDS matrices,which is especially desired for industrial applications demanding highly efficient models.展开更多
High-dimensional and sparse(HiDS)matrices commonly arise in various industrial applications,e.g.,recommender systems(RSs),social networks,and wireless sensor networks.Since they contain rich information,how to accurat...High-dimensional and sparse(HiDS)matrices commonly arise in various industrial applications,e.g.,recommender systems(RSs),social networks,and wireless sensor networks.Since they contain rich information,how to accurately represent them is of great significance.A latent factor(LF)model is one of the most popular and successful ways to address this issue.Current LF models mostly adopt L2-norm-oriented Loss to represent an HiDS matrix,i.e.,they sum the errors between observed data and predicted ones with L2-norm.Yet L2-norm is sensitive to outlier data.Unfortunately,outlier data usually exist in such matrices.For example,an HiDS matrix from RSs commonly contains many outlier ratings due to some heedless/malicious users.To address this issue,this work proposes a smooth L1-norm-oriented latent factor(SL-LF)model.Its main idea is to adopt smooth L1-norm rather than L2-norm to form its Loss,making it have both strong robustness and high accuracy in predicting the missing data of an HiDS matrix.Experimental results on eight HiDS matrices generated by industrial applications verify that the proposed SL-LF model not only is robust to the outlier data but also has significantly higher prediction accuracy than state-of-the-art models when they are used to predict the missing data of HiDS matrices.展开更多
Traditional matrix does not allow matrix-assisted laser desorption/ionization mass spectrometry(MALDI MS) to analyze volatile compounds,because volatile analytes may vaporize during the sample preparation process or i...Traditional matrix does not allow matrix-assisted laser desorption/ionization mass spectrometry(MALDI MS) to analyze volatile compounds,because volatile analytes may vaporize during the sample preparation process or in the high vacuum circumstance of ion source.Herein,we reported a Co and N doped porous carbon material(Co-NC) which were synthesized by pyrolysis of a Schiff base coordination compound.Co-NC could simultaneously act as adsorbent of volatile compounds and as matrix of MALDI MS,to provide the capability of MALDI MS to analyze volatile compounds.As adsorbent,Co-NC could stro ngly adsorb and enrich the volatile compounds in perfume and herbs,and hold them even in the high vacuum circumstance.On the other hand,Co-NC could absorb the energy of the laser,and then transfer the energy to the analyte for desorption and ionization of analyte in both negative and positive ionization modes.Additionally,the background interferences were avoided in the low-mass region(<500 Da) when using Co-NC as matrix,overcoming the challenges of MALDI MS analysis of small molecule compounds.In summary,Co-NC as matrix tremendously extended the application of MALDI MS.展开更多
Sudden and uncertain events often cause cross-contagion of risk among various sectors of the macroeconomy.This paper introduces the stochastic volatility shock that follows a thick-tailed Student’s t-distribution int...Sudden and uncertain events often cause cross-contagion of risk among various sectors of the macroeconomy.This paper introduces the stochastic volatility shock that follows a thick-tailed Student’s t-distribution into a high-order approximate dynamic stochastic general equilibrium(DSGE)model with Epstein–Zin preference to better analyze the dynamic effect of uncertainty risk on macroeconomics.Then,the high-dimensional DSGE model(DSGE-SV-t)is developed to examine the impact of uncertainty risk on the transmission mechanism among macroeconomic sectors.The empirical research found that uncertainty risk generates heterogeneous impacts on macroeconomic dynamics under different inflation levels and economic states.Among them,a technological shock has the strongest impact on employment and consumption channels.The crowding-out effect of a fiscal policy stimulus on consumption and private investments is relatively weakened when considering uncertainty risk but is more pronounced during periods of high inflation.Uncertainty risk can partly explain the decline in investments and the increase in interest rates and employment rates,given the impact of an agent’s risk preferences.Compared with external economic conditions,the inflation factor has a stronger impact on the macro transmission mechanism caused by uncertainty risk.展开更多
The estimation of covariance matrices is very important in many fields, such as statistics. In real applications, data are frequently influenced by high dimensions and noise. However, most relevant studies are based o...The estimation of covariance matrices is very important in many fields, such as statistics. In real applications, data are frequently influenced by high dimensions and noise. However, most relevant studies are based on complete data. This paper studies the optimal estimation of high-dimensional covariance matrices based on missing and noisy sample under the norm. First, the model with sub-Gaussian additive noise is presented. The generalized sample covariance is then modified to define a hard thresholding estimator , and the minimax upper bound is derived. After that, the minimax lower bound is derived, and it is concluded that the estimator presented in this article is rate-optimal. Finally, numerical simulation analysis is performed. The result shows that for missing samples with sub-Gaussian noise, if the true covariance matrix is sparse, the hard thresholding estimator outperforms the traditional estimate method.展开更多
Initial success has been achieved in Hong Kong in controlling primary air pollutants,but ambient ozone levels kept increasing during the past three decades.Volatile organic compounds(VOCs)are important for mitigating ...Initial success has been achieved in Hong Kong in controlling primary air pollutants,but ambient ozone levels kept increasing during the past three decades.Volatile organic compounds(VOCs)are important for mitigating ozone pollution as its major precursors.This study analyzed VOC characteristics of roadside,suburban,and rural sites in Hong Kong to investigate their compositions,concentrations,and source contributions.Herewe showthat the TVOC concentrations were 23.05±13.24,12.68±15.36,and 5.16±5.48 ppbv for roadside,suburban,and rural sites between May 2015 to June 2019,respectively.By using Positive Matrix Factorization(PMF)model,six sources were identified at the roadside site over five years:Liquefied petroleum gas(LPG)usage(33%–46%),gasoline evaporation(8%–31%),aged air mass(11%–28%),gasoline exhaust(5%–16%),diesel exhaust(2%–16%)and fuel filling(75–9%).Similarly,six sources were distinguished at the suburban site,including LPG usage(30%–33%),solvent usage(20%–26%),diesel exhaust(14%–26%),gasoline evaporation(8%–16%),aged air mass(4%–11%),and biogenic emissions(2%–5%).At the rural site,four sources were identified,including aged airmass(33%–51%),solvent usage(25%–30%),vehicular emissions(11%–28%),and biogenic emissions(6%–12%).The analysis further revealed that fuel filling and LPG usage were the primary contributors to OFP and OH reactivity at the roadside site,while solvent usage and biogenic emissions accounted for almost half of OFP and OH reactivity at the suburban and rural sites,respectively.These findings highlight the importance of identifying and characterizing VOC sources at different sites to help policymakers develop targeted measures for pollution mitigation in specific areas.展开更多
Tianjin is the third largest megacity and the fastest growth area in China,and consequently faces the problems of surface ozone and haze episodes.This study measures and characterizes volatile organic compounds （VOCs...Tianjin is the third largest megacity and the fastest growth area in China,and consequently faces the problems of surface ozone and haze episodes.This study measures and characterizes volatile organic compounds （VOCs）,which are ozone precursors,to identify their possible sources and evaluate their contribution to ozone formation in urban and suburban Tianjin,China during the HaChi （Haze in China） summer campaign in 2009.A total of 107 species of ambient VOCs were detected,and the average concentrations of VOCs at urban and suburban sites were 92 and 174 ppbv,respectively.Of those,51 species of VOCs were extracted to analyze the possible VOC sources using positive matrix factorization.The identified sources of VOCs were significantly related to vehicular activities,which specifically contributed 60％ to urban and 42％ to suburban VOCs loadings in Tianjin.Industrial emission was the second most prominent source of ambient VOCs in both urban and suburban areas,although the contribution of industry in the suburban area （36％） was much higher than that at the urban area （16％）.We conclude that controlling vehicle emissions should be a top priority for VOC reduction,and that fast industrialization and urbanization causes air pollution to be more complex due to the combined emission of VOCs from industry and daily life,especially in suburban areas.展开更多
Based on one-year observation,the concentration,sources,and potential source areas of volatile organic compounds(VOCs)were comprehensively analyzed to investigate the pollution characteristics of ambient VOCs in Haiko...Based on one-year observation,the concentration,sources,and potential source areas of volatile organic compounds(VOCs)were comprehensively analyzed to investigate the pollution characteristics of ambient VOCs in Haikou,China.The results showed that the annual average concentration of total VOCs(TVOCs)was 11.4 ppb V,and the composition was dominated by alkanes(8.2 ppb V,71.4%)and alkenes(1.3 ppb V,20.5%).The diurnal variation in the concentration of dominant VOC species showed a distinct bimodal distribution with peaks in the morning and evening.The greatest contribution to ozone formation potential(OFP)was made by alkenes(51.6%),followed by alkanes(27.2%).The concentrations of VOCs and nitrogen dioxide(NO_(2))in spring and summer were low,and it was difficult to generate high ozone(O_(3))concentrations through photochemical reactions.The significant increase in O_(3)concentrations in autumn and winter was mainly related to the transmission of pollutants from the northeast.Traffic sources(40.1%),industrial sources(19.4%),combustion sources(18.6%),solvent usage sources(15.5%)and plant sources(6.4%)were identified as major sources of VOCs through the positive matrix factorization(PMF)model.The southeastern coastal areas of China were identified as major potential source areas of VOCs through the potential source contribution function(PSCF)and concentration-weighted trajectory(CWT)models.Overall,the concentration of ambient VOCs in Haikou was strongly influenced by traffic sources and long-distance transport,and the control of VOCs emitted from vehicles should be strengthened to reduce the active species of ambient VOCs in Haikou,thereby reducing the generation of O_(3).展开更多
In recent years,many cities have taken measures to reduce volatile organic compounds(VOCs),an important precursor of ozone(O_(3)),to alleviate O_(3) pollution in China.116 VOC species were measured by online and offli...In recent years,many cities have taken measures to reduce volatile organic compounds(VOCs),an important precursor of ozone(O_(3)),to alleviate O_(3) pollution in China.116 VOC species were measured by online and offline methods in the urban area of Jiaozuo from May to October in 2021 to analyze the compositional characteristics.VOC sources were analyzed by a positive matrix factorization(PMF)model,and the sensitivity of ozone generation was determined by ozone isopleth plotting research(OZIPR)simulation.The results showed that the average volume concentration of total VOCs was 30.54 ppbv and showed a bimodal feature due to the rush-hour traffic in the morning and at nightfall.The most dominant VOC groups were oxygenated VOCs(OVOCs,29.3%)and alkanes(26.7%),and the most abundant VOC species were acetone and acetylene.However,based on the maximum incremental reactivity(MIR)method,the major VOC groups in terms of ozone formation potential(OFP)contribution were OVOCs(68.09μg/m^(3),31.5%),aromatics(62.90μg/m^(3),29.1%)and alkene/alkynes(54.90μg/m^(3),25.4%).This indicates that the control of OVOCs,aromatics and alkene/alkynes should take priority.Five sources of VOCs were quantified by PMF,including fixed sources of fossil fuel combustion(27.8%),industrial processes(25.9%),vehicle exhaust(19.7%),natural and secondary formation(13.9%)and solvent usage(12.7%).The empirical kinetic modeling approach(EKMA)curve obtained by OZIPR on O_(3) exceedance days indicated that the O_(3) sensitivity varied in different months.The results provide theoretical support for O_(3) pollution prevention and control in Jiaozuo.展开更多
The petroleum industry is a significant source of anthropogenic volatile organic compounds(VOCs),but up to now,its exact impact on urban VOCs and ozone(O_(3))remains unclear.This study conducted year-long VOC ob-serva...The petroleum industry is a significant source of anthropogenic volatile organic compounds(VOCs),but up to now,its exact impact on urban VOCs and ozone(O_(3))remains unclear.This study conducted year-long VOC ob-servations in Dongying,China,a petroleum industrial region.The VOCs from the petroleum industry(oil and gas volatilization and petrochemical production)were identified by employing the positive matrix factorization model,and their contribution to O_(3) formation was quantitatively evaluated using an observation-based chemical box model.The observed annual average concentration of VOCs was 68.6±63.5 ppbv,with a maximum daily av-erage of 335.3 ppbv.The petroleum industry accounted for 66.5%of total VOCs,contributing 54.9%from oil and gas evaporation and 11.6%from petrochemical production.Model results indicated that VOCs from the petroleum industry contributed to 31%of net O_(3) production,with 21.3%and 34.2%contributions to HO_(2)+NO and RO_(2)+NO pathways,respectively.The larger impact on the RO_(2) pathway is primarily due to the fact that OH+VOCs ac-count for 86.9%of the primary source of RO_(2).This study highlights the critical role of controlling VOCs from the petroleum industry in urban O_(3) pollution,especially those from previously overlooked low-reactivity alkanes.展开更多
We investigate the structure of a large precision matrix in Gaussian graphical models by decomposing it into a low rank component and a remainder part with sparse precision matrix.Based on the decomposition,we propose...We investigate the structure of a large precision matrix in Gaussian graphical models by decomposing it into a low rank component and a remainder part with sparse precision matrix.Based on the decomposition,we propose to estimate the large precision matrix by inverting a principal orthogonal decomposition(IPOD).The IPOD approach has appealing practical interpretations in conditional graphical models given the low rank component,and it connects to Gaussian graphical models with latent variables.Specifically,we show that the low rank component in the decomposition of the large precision matrix can be viewed as the contribution from the latent variables in a Gaussian graphical model.Compared with existing approaches for latent variable graphical models,the IPOD is conveniently feasible in practice where only inverting a low-dimensional matrix is required.To identify the number of latent variables,which is an objective of its own interest,we investigate and justify an approach by examining the ratios of adjacent eigenvalues of the sample covariance matrix?Theoretical properties,numerical examples,and a real data application demonstrate the merits of the IPOD approach in its convenience,performance,and interpretability.展开更多
基金supported by the National Natural Science Foundation of China under Grant Nos.72271232,71873137the MOE Project of Key Research Institute of Humanities and Social Sciences under Grant No.22JJD110001+1 种基金the support of Public Computing CloudRenmin University of China。
文摘The estimates of the high-dimensional volatility matrix based on high-frequency data play a pivotal role in many financial applications.However,most existing studies have been built on the sub-Gaussian and cross-sectional independence assumptions of microstructure noise,which are typically violated in the financial markets.In this paper,the authors proposed a new robust volatility matrix estimator,with very mild assumptions on the cross-sectional dependence and tail behaviors of the noises,and demonstrated that it can achieve the optimal convergence rate n-1/4.Furthermore,the proposed model offered better explanatory and predictive powers by decomposing the estimator into low-rank and sparse components,using an appropriate regularization procedure.Simulation studies demonstrated that the proposed estimator outperforms its competitors under various dependence structures of microstructure noise.Additionally,an extensive analysis of the high-frequency data for stocks in the Shenzhen Stock Exchange of China demonstrated the practical effectiveness of the estimator.
基金supported by the National Key R&D Program of China(No.2023YFC3705801)the National Natural Science Foundation of China(No.42177085).
文摘Substantial effects of photochemical reaction losses of volatile organic compounds(VOCs)on factor profiles can be investigated by comparing the differences between daytime and nighttime dispersion-normalized VOC data resolved profiles.Hourly speciated VOC data measured in Shijiazhuang,China from May to September 2021 were used to conduct study.The mean VOC concentration in the daytime and at nighttime were 32.8 and 36.0 ppbv,respectively.Alkanes and aromatics concentrations in the daytime(12.9 and 3.08 ppbv)were lower than nighttime(15.5 and 3.63 ppbv),whereas that of alkenes showed the opposite tendency.The concentration differences between daytime and nighttime for alkynes and halogenated hydrocarbonswere uniformly small.The reactivities of the dominant species in factor profiles for gasoline emissions,natural gas and diesel vehicles,and liquefied petroleum gas were relatively low and their profiles were less affected by photochemical losses.Photochemical losses produced a substantial impact on the profiles of solvent use,petrochemical industry emissions,combustion sources,and biogenic emissions where the dominant species in these factor profiles had high reactivities.Although the profile of biogenic emissions was substantially affected by photochemical loss of isoprene,the low emissions at nighttime also had an important impact on its profile.Chemical losses of highly active VOC species substantially reduced their concentrations in apportioned factor profiles.This study results were consistent with the analytical results obtained through initial concentration estimation,suggesting that the initial concentration estimation could be the most effective currently availablemethod for the source analyses of active VOCs although with uncertainty.
基金supported in part by the National Natural Science Foundation of China (6177249391646114)+1 种基金Chongqing research program of technology innovation and application (cstc2017rgzn-zdyfX0020)in part by the Pioneer Hundred Talents Program of Chinese Academy of Sciences
文摘Latent factor(LF)models are highly effective in extracting useful knowledge from High-Dimensional and Sparse(HiDS)matrices which are commonly seen in various industrial applications.An LF model usually adopts iterative optimizers,which may consume many iterations to achieve a local optima,resulting in considerable time cost.Hence,determining how to accelerate the training process for LF models has become a significant issue.To address this,this work proposes a randomized latent factor(RLF)model.It incorporates the principle of randomized learning techniques from neural networks into the LF analysis of HiDS matrices,thereby greatly alleviating computational burden.It also extends a standard learning process for randomized neural networks in context of LF analysis to make the resulting model represent an HiDS matrix correctly.Experimental results on three HiDS matrices from industrial applications demonstrate that compared with state-of-the-art LF models,RLF is able to achieve significantly higher computational efficiency and comparable prediction accuracy for missing data.I provides an important alternative approach to LF analysis of HiDS matrices,which is especially desired for industrial applications demanding highly efficient models.
基金supported in part by the National Natural Science Foundation of China(61702475,61772493,61902370,62002337)in part by the Natural Science Foundation of Chongqing,China(cstc2019jcyj-msxmX0578,cstc2019jcyjjqX0013)+1 种基金in part by the Chinese Academy of Sciences“Light of West China”Program,in part by the Pioneer Hundred Talents Program of Chinese Academy of Sciencesby Technology Innovation and Application Development Project of Chongqing,China(cstc2019jscx-fxydX0027)。
文摘High-dimensional and sparse(HiDS)matrices commonly arise in various industrial applications,e.g.,recommender systems(RSs),social networks,and wireless sensor networks.Since they contain rich information,how to accurately represent them is of great significance.A latent factor(LF)model is one of the most popular and successful ways to address this issue.Current LF models mostly adopt L2-norm-oriented Loss to represent an HiDS matrix,i.e.,they sum the errors between observed data and predicted ones with L2-norm.Yet L2-norm is sensitive to outlier data.Unfortunately,outlier data usually exist in such matrices.For example,an HiDS matrix from RSs commonly contains many outlier ratings due to some heedless/malicious users.To address this issue,this work proposes a smooth L1-norm-oriented latent factor(SL-LF)model.Its main idea is to adopt smooth L1-norm rather than L2-norm to form its Loss,making it have both strong robustness and high accuracy in predicting the missing data of an HiDS matrix.Experimental results on eight HiDS matrices generated by industrial applications verify that the proposed SL-LF model not only is robust to the outlier data but also has significantly higher prediction accuracy than state-of-the-art models when they are used to predict the missing data of HiDS matrices.
基金supported by the National Key R&D Program of China(No.2018YFA0506900)National Natural Science Foundation of China(Nos.21635008 and 21621062)the Military Medicine and Healthy Major Project,China(No.AWS16J016)。
文摘Traditional matrix does not allow matrix-assisted laser desorption/ionization mass spectrometry(MALDI MS) to analyze volatile compounds,because volatile analytes may vaporize during the sample preparation process or in the high vacuum circumstance of ion source.Herein,we reported a Co and N doped porous carbon material(Co-NC) which were synthesized by pyrolysis of a Schiff base coordination compound.Co-NC could simultaneously act as adsorbent of volatile compounds and as matrix of MALDI MS,to provide the capability of MALDI MS to analyze volatile compounds.As adsorbent,Co-NC could stro ngly adsorb and enrich the volatile compounds in perfume and herbs,and hold them even in the high vacuum circumstance.On the other hand,Co-NC could absorb the energy of the laser,and then transfer the energy to the analyte for desorption and ionization of analyte in both negative and positive ionization modes.Additionally,the background interferences were avoided in the low-mass region(<500 Da) when using Co-NC as matrix,overcoming the challenges of MALDI MS analysis of small molecule compounds.In summary,Co-NC as matrix tremendously extended the application of MALDI MS.
基金supported by the National Natural Science Foundation of China(Nos.72141304,71790594,71901130)。
文摘Sudden and uncertain events often cause cross-contagion of risk among various sectors of the macroeconomy.This paper introduces the stochastic volatility shock that follows a thick-tailed Student’s t-distribution into a high-order approximate dynamic stochastic general equilibrium(DSGE)model with Epstein–Zin preference to better analyze the dynamic effect of uncertainty risk on macroeconomics.Then,the high-dimensional DSGE model(DSGE-SV-t)is developed to examine the impact of uncertainty risk on the transmission mechanism among macroeconomic sectors.The empirical research found that uncertainty risk generates heterogeneous impacts on macroeconomic dynamics under different inflation levels and economic states.Among them,a technological shock has the strongest impact on employment and consumption channels.The crowding-out effect of a fiscal policy stimulus on consumption and private investments is relatively weakened when considering uncertainty risk but is more pronounced during periods of high inflation.Uncertainty risk can partly explain the decline in investments and the increase in interest rates and employment rates,given the impact of an agent’s risk preferences.Compared with external economic conditions,the inflation factor has a stronger impact on the macro transmission mechanism caused by uncertainty risk.
文摘The estimation of covariance matrices is very important in many fields, such as statistics. In real applications, data are frequently influenced by high dimensions and noise. However, most relevant studies are based on complete data. This paper studies the optimal estimation of high-dimensional covariance matrices based on missing and noisy sample under the norm. First, the model with sub-Gaussian additive noise is presented. The generalized sample covariance is then modified to define a hard thresholding estimator , and the minimax upper bound is derived. After that, the minimax lower bound is derived, and it is concluded that the estimator presented in this article is rate-optimal. Finally, numerical simulation analysis is performed. The result shows that for missing samples with sub-Gaussian noise, if the true covariance matrix is sparse, the hard thresholding estimator outperforms the traditional estimate method.
基金supported by Hong Kong Environment Protection Department(Quotation Ref.18-06532)Hong Kong Innovation and Technology Fund(ITS/193/20FP)Hong Kong Research Grants Council(No.26304921).
文摘Initial success has been achieved in Hong Kong in controlling primary air pollutants,but ambient ozone levels kept increasing during the past three decades.Volatile organic compounds(VOCs)are important for mitigating ozone pollution as its major precursors.This study analyzed VOC characteristics of roadside,suburban,and rural sites in Hong Kong to investigate their compositions,concentrations,and source contributions.Herewe showthat the TVOC concentrations were 23.05±13.24,12.68±15.36,and 5.16±5.48 ppbv for roadside,suburban,and rural sites between May 2015 to June 2019,respectively.By using Positive Matrix Factorization(PMF)model,six sources were identified at the roadside site over five years:Liquefied petroleum gas(LPG)usage(33%–46%),gasoline evaporation(8%–31%),aged air mass(11%–28%),gasoline exhaust(5%–16%),diesel exhaust(2%–16%)and fuel filling(75–9%).Similarly,six sources were distinguished at the suburban site,including LPG usage(30%–33%),solvent usage(20%–26%),diesel exhaust(14%–26%),gasoline evaporation(8%–16%),aged air mass(4%–11%),and biogenic emissions(2%–5%).At the rural site,four sources were identified,including aged airmass(33%–51%),solvent usage(25%–30%),vehicular emissions(11%–28%),and biogenic emissions(6%–12%).The analysis further revealed that fuel filling and LPG usage were the primary contributors to OFP and OH reactivity at the roadside site,while solvent usage and biogenic emissions accounted for almost half of OFP and OH reactivity at the suburban and rural sites,respectively.These findings highlight the importance of identifying and characterizing VOC sources at different sites to help policymakers develop targeted measures for pollution mitigation in specific areas.
基金supported by the Tianjin Fundamental Research Program of the Tianjin Committee of Science and Technology (Grant No. 10JCYBJC050800)the National Special Science and Technology Program for Non-Profit Industry of the Ministry of Environmental Protection (Grant No. 200909022)+2 种基金the 973 Program (Grant No. 2011CB403402)the National Natural Science Foundation of China (NSFC) (Grant No. 40875001)the Basic Research Fund of the Chinese Academy of Meteorological Sciences (Grant No. 2008Z011)
文摘Tianjin is the third largest megacity and the fastest growth area in China,and consequently faces the problems of surface ozone and haze episodes.This study measures and characterizes volatile organic compounds （VOCs）,which are ozone precursors,to identify their possible sources and evaluate their contribution to ozone formation in urban and suburban Tianjin,China during the HaChi （Haze in China） summer campaign in 2009.A total of 107 species of ambient VOCs were detected,and the average concentrations of VOCs at urban and suburban sites were 92 and 174 ppbv,respectively.Of those,51 species of VOCs were extracted to analyze the possible VOC sources using positive matrix factorization.The identified sources of VOCs were significantly related to vehicular activities,which specifically contributed 60％ to urban and 42％ to suburban VOCs loadings in Tianjin.Industrial emission was the second most prominent source of ambient VOCs in both urban and suburban areas,although the contribution of industry in the suburban area （36％） was much higher than that at the urban area （16％）.We conclude that controlling vehicle emissions should be a top priority for VOC reduction,and that fast industrialization and urbanization causes air pollution to be more complex due to the combined emission of VOCs from industry and daily life,especially in suburban areas.
基金supported by the Major Program of Science and Technology of Hainan Province,China(No.ZDKJ202007)the Special Foundation of Government Financial of Hainan Province,China(No.ZC2018-196)the Youth Innovation Foundation of Hainan Research Academy of Environmental Sciences,China(No.QNCX2021002)。
文摘Based on one-year observation,the concentration,sources,and potential source areas of volatile organic compounds(VOCs)were comprehensively analyzed to investigate the pollution characteristics of ambient VOCs in Haikou,China.The results showed that the annual average concentration of total VOCs(TVOCs)was 11.4 ppb V,and the composition was dominated by alkanes(8.2 ppb V,71.4%)and alkenes(1.3 ppb V,20.5%).The diurnal variation in the concentration of dominant VOC species showed a distinct bimodal distribution with peaks in the morning and evening.The greatest contribution to ozone formation potential(OFP)was made by alkenes(51.6%),followed by alkanes(27.2%).The concentrations of VOCs and nitrogen dioxide(NO_(2))in spring and summer were low,and it was difficult to generate high ozone(O_(3))concentrations through photochemical reactions.The significant increase in O_(3)concentrations in autumn and winter was mainly related to the transmission of pollutants from the northeast.Traffic sources(40.1%),industrial sources(19.4%),combustion sources(18.6%),solvent usage sources(15.5%)and plant sources(6.4%)were identified as major sources of VOCs through the positive matrix factorization(PMF)model.The southeastern coastal areas of China were identified as major potential source areas of VOCs through the potential source contribution function(PSCF)and concentration-weighted trajectory(CWT)models.Overall,the concentration of ambient VOCs in Haikou was strongly influenced by traffic sources and long-distance transport,and the control of VOCs emitted from vehicles should be strengthened to reduce the active species of ambient VOCs in Haikou,thereby reducing the generation of O_(3).
基金supported by the Research Project Entrusted by Henan Ecological Environment Monitoring and Safety Center,China(No.20201557)the Study of Collaborative Prevention and Control of Fine Particulate Matter and Ozone Pollution of Jiaozuo(No.DQGG202134)。
文摘In recent years,many cities have taken measures to reduce volatile organic compounds(VOCs),an important precursor of ozone(O_(3)),to alleviate O_(3) pollution in China.116 VOC species were measured by online and offline methods in the urban area of Jiaozuo from May to October in 2021 to analyze the compositional characteristics.VOC sources were analyzed by a positive matrix factorization(PMF)model,and the sensitivity of ozone generation was determined by ozone isopleth plotting research(OZIPR)simulation.The results showed that the average volume concentration of total VOCs was 30.54 ppbv and showed a bimodal feature due to the rush-hour traffic in the morning and at nightfall.The most dominant VOC groups were oxygenated VOCs(OVOCs,29.3%)and alkanes(26.7%),and the most abundant VOC species were acetone and acetylene.However,based on the maximum incremental reactivity(MIR)method,the major VOC groups in terms of ozone formation potential(OFP)contribution were OVOCs(68.09μg/m^(3),31.5%),aromatics(62.90μg/m^(3),29.1%)and alkene/alkynes(54.90μg/m^(3),25.4%).This indicates that the control of OVOCs,aromatics and alkene/alkynes should take priority.Five sources of VOCs were quantified by PMF,including fixed sources of fossil fuel combustion(27.8%),industrial processes(25.9%),vehicle exhaust(19.7%),natural and secondary formation(13.9%)and solvent usage(12.7%).The empirical kinetic modeling approach(EKMA)curve obtained by OZIPR on O_(3) exceedance days indicated that the O_(3) sensitivity varied in different months.The results provide theoretical support for O_(3) pollution prevention and control in Jiaozuo.
基金funded by the National Natural Science Foundation of China[grant number 42075094]the China Postdoctoral Science Foundation[grant number 2021M691921]+1 种基金the Ministry of Ecology and Environment of the People’s Republic of China[grant number DQGG202121]the Dongying Ecological and Environmental Bureau[grant number 2021DFKY-0779]。
文摘The petroleum industry is a significant source of anthropogenic volatile organic compounds(VOCs),but up to now,its exact impact on urban VOCs and ozone(O_(3))remains unclear.This study conducted year-long VOC ob-servations in Dongying,China,a petroleum industrial region.The VOCs from the petroleum industry(oil and gas volatilization and petrochemical production)were identified by employing the positive matrix factorization model,and their contribution to O_(3) formation was quantitatively evaluated using an observation-based chemical box model.The observed annual average concentration of VOCs was 68.6±63.5 ppbv,with a maximum daily av-erage of 335.3 ppbv.The petroleum industry accounted for 66.5%of total VOCs,contributing 54.9%from oil and gas evaporation and 11.6%from petrochemical production.Model results indicated that VOCs from the petroleum industry contributed to 31%of net O_(3) production,with 21.3%and 34.2%contributions to HO_(2)+NO and RO_(2)+NO pathways,respectively.The larger impact on the RO_(2) pathway is primarily due to the fact that OH+VOCs ac-count for 86.9%of the primary source of RO_(2).This study highlights the critical role of controlling VOCs from the petroleum industry in urban O_(3) pollution,especially those from previously overlooked low-reactivity alkanes.
文摘We investigate the structure of a large precision matrix in Gaussian graphical models by decomposing it into a low rank component and a remainder part with sparse precision matrix.Based on the decomposition,we propose to estimate the large precision matrix by inverting a principal orthogonal decomposition(IPOD).The IPOD approach has appealing practical interpretations in conditional graphical models given the low rank component,and it connects to Gaussian graphical models with latent variables.Specifically,we show that the low rank component in the decomposition of the large precision matrix can be viewed as the contribution from the latent variables in a Gaussian graphical model.Compared with existing approaches for latent variable graphical models,the IPOD is conveniently feasible in practice where only inverting a low-dimensional matrix is required.To identify the number of latent variables,which is an objective of its own interest,we investigate and justify an approach by examining the ratios of adjacent eigenvalues of the sample covariance matrix?Theoretical properties,numerical examples,and a real data application demonstrate the merits of the IPOD approach in its convenience,performance,and interpretability.
