Health risk assessment of trace metal(loid)s in agricultural soils based on Monte Carlo simulation coupled with positive matrix factorization model in Chongqing, southwest China

This study aimed to investigate the pollution characteristics, source apportionment, and health risks associated with trace metal(loid)s(TMs) in the major agricultural producing areas in Chongqing, China. We analyzed the source apportionment and assessed the health risk of TMs in agricultural soils by using positive matrix factorization(PMF) model and health risk assessment(HRA) model based on Monte Carlo simulation. Meanwhile, we combined PMF and HRA models to explore the health risks of TMs in agricultural soils by different pollution sources to determine the priority control factors. Results showed that the average contents of cadmium(Cd), arsenic (As), lead(Pb), chromium(Cr), copper(Cu), nickel(Ni), and zinc(Zn) in the soil were found to be 0.26, 5.93, 27.14, 61.32, 23.81, 32.45, and 78.65 mg/kg, respectively. Spatial analysis and source apportionment analysis revealed that urban and industrial sources, agricultural sources, and natural sources accounted for 33.0%, 27.7%, and 39.3% of TM accumulation in the soil, respectively. In the HRA model based on Monte Carlo simulation, noncarcinogenic risks were deemed negligible(hazard index <1), the carcinogenic risks were at acceptable level(10^(-6)<total carcinogenic risk ≤ 10^(-4)), with higher risks observed for children compared to adults. The relationship between TMs, their sources, and health risks indicated that urban and industrial sources were primarily associated with As, contributing to 75.1% of carcinogenic risks and 55.7% of non-carcinogenic risks, making them the primary control factors. Meanwhile, agricultural sources were primarily linked to Cd and Pb, contributing to 13.1% of carcinogenic risks and 21.8% of non-carcinogenic risks, designating them as secondary control factors.

A MATRIX EQUATION FROM AN INVERSE PROBLEM OF VIBRATION THEORY

The symmetric,positive semidefinite,and positive definite real solutions of the matrix equation XA=YAD from an inverse problem of vibration theory are considered.When D=T the necessary and sufficient conditions for the existence of such solutions and their general forms are derived.

上海某居民区恶臭污染溯源、臭氧生成潜势及健康风险评估

针对上海某居民区恶臭投诉问题,对恶臭物质来源、特征及其风险评估展开相关研究。利用正定矩阵因子分解(positive matrix factorization,PMF)模型对居民区恶臭点连续监测数据进行来源解析,共识别出3个排放源,分别为餐厨垃圾源(41.24%)、农业源(32.85%)、二次生成和尾气排放混合源(25.91%),确定餐厨垃圾源为居民区恶臭的主要来源。通过分析监测点位间主要致臭物质异味活度值(odor active value,OAV)的相关性,可知居民区受到餐厨垃圾预处理车间和生化车间的恶臭污染为主,全天受影响程度具有间歇式变化特征。通过计算预处理、生化处理和深加工3个车间排气筒的恶臭物质的臭氧生成潜势(ozone formation potential,OFP),可知各物质种类对OFP的贡献率从大到小依次为羰基类(37.46%)、醇类(21.38%)、烯烃类(15.52%)、挥发性脂肪酸类(13.70%)、芳香烃类(4.02%)、含氮化合物(3.90%)、烷烃类(1.77%)、酯类(1.29%)、硫化物(0.95%)、含氯有机物(0.01%),羰基类、醇类、烯烃类、挥发性脂肪酸类为该厂区OFP贡献的代表物质种类。各车间的OFP从高到低依次为预处理车间(27051.82μg/m^(3))、生化车间(7547.51μg/m^(3))、深加工车间(1647.14μg/m^(3)),乙醛、丙烯酸、1-丁烯、甲醛、乙醇、丙烯和苯酚对OFP的贡献率在3个排气筒中均较高,是该餐厨垃圾处理厂区生成臭氧的主要污染物质,并且乙醛对OFP的贡献率最高。敏感点恶臭物质的健康风险评估结果表明,监测期间累计非致癌风险指数与致癌风险指数均超过美国国家环境保护局(U.S.Environmental Protection Agency,U.S.EPA)推荐的安全阈值,主要贡献物质分别为丙烯醛和甲醛。因此,乙醛、丙烯醛和甲醛应作为餐厨垃圾处理厂中恶臭物质的优先监控指标。

奇异鞍点问题中广义位移分裂迭代方法的半收敛性分析

Recently,some authors(Shen and Shi,2016)studied the generalized shiftsplitting(GSS)iteration method for singular saddle point problem with nonsymmetric positive definite(1,1)-block and symmetric positive semidefinite(2,2)-block.In this paper,we further apply the GSS iteration method to solve singular saddle point problem with nonsymmetric positive semidefinite(1,1)-block and symmetric positive semidefinite(2,2)-block,prove the semi-convergence of the GSS iteration method and analyze the spectral properties of the corresponding preconditioned matrix.Numerical experiment is given to indicate that the GSS iteration method with appropriate iteration parameters is effective and competitive for practical use.

RF-PSSM:A Combination of Rotation Forest Algorithm and Position-Specific Scoring Matrix for Improved Prediction of Protein-Protein Interactions Between Hepatitis C Virus and Human

The identification of hepatitis C virus(HCV)virus-human protein interactions will not only help us understand the molecular mechanisms of related diseases but also be conductive to discovering new drug targets.An increasing number of clinically and experimentally validated interactions between HCV and human proteins have been documented in public databases,facilitating studies based on computational methods.In this study,we proposed a new computational approach,rotation forest position-specific scoring matrix(RF-PSSM),to predict the interactions among HCV and human proteins.In particular,PSSM was used to characterize each protein,two-dimensional principal component analysis(2DPCA)was then adopted for feature extraction of PSSM.Finally,rotation forest(RF)was used to implement classification.The results of various ablation experiments show that on independent datasets,the accuracy and area under curve(AUC)value of RF-PSSM can reach 93.74% and 94.29%,respectively,outperforming almost all cutting-edge research.In addition,we used RF-PSSM to predict 9 human proteins that may interact with HCV protein E1,which can provide theoretical guidance for future experimental studies.

华北典型农业区PM_(2.5)组分分析与来源解析

华北大气污染区域化正在对农业生态区域产生显著影响,为了了解华北农业地区大气细颗粒物PM_(2.5)的季节分布特征,2017年7月、9月、12月以及2018年4月在中国科学院禹城农业生态综合实验站进行分季节PM_(2.5)样品采集,并测定分析了样品中31种化学成分。结果表明,碳质气溶胶总体的浓度水平为13.11±8.37μg m^(−3),有机碳(OC)冬春季节浓度较高,元素碳(EC)浓度在秋冬季节较高。同时OC/EC的比值在秋季明显偏低,表明在秋季二次碳质气溶胶对PM_(2.5)贡献较小。水溶性离子浓度总体在冬季最高。NO_(3)^(-)/SO_(4)^(2-)比值在夏季明显偏低为0.69,华北地区夏季固定点源对大气污染的贡献相对较高。PM_(2.5)中金属元素以Na、Mg、Al、Ca、K、Fe等地壳元素为主,具有致癌风险的Co、Cr、Ni、Pb、As等金属元素年均浓度为0.32±0.24 ng m^(−3)、5.40±5.42 ng m^(−3)、10.23±7.46 ng m^(−3)、42.23±27.75 ng m^(−3)、5.66±3.79 ng m^(−3)。受体模型(PMF)计算结果表明,PM_(2.5)的主要来源为二次污染源、生物质燃烧源、燃煤燃油源、柴油车尾气和土壤源,贡献率分别达37.1%、18.2%、14.2%、9.4%和7.9%,表明农业区细颗粒物污染受到华北工业、农业与自然排放的多重影响。

用于飞机蒙皮成形的可重构多点柔性工装设计

针对飞机蒙皮等大型薄壁板类零件的外形复杂、曲率变化大、刚度低等特点,设计了可重构多点柔性工装,该柔性工装通过其精确定位和保形功能,可用于蒙皮镜像铣切、蒙皮零件的数控切边等,应用范围十分广泛。

Characterization and Source Apportionment of Volatile Organic Compounds in Urban and Suburban Tianjin, China

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.

Eigenvector Method for Ranking Alternatives with Vague Value Measurements

An eigenvector method for ranking alternatives whose measurements are given as vague values is provided. Firstly, a positive matrix is constructed which is defined as evaluation information matrix （EIM）. Based on four assumptions for evaluating alternatives, a ranking eigenvector is defined. And then it is proved, based on positive matrix theory, that the EIM＇s eigenvector corresponding to the maximal eigenvalue is the ranking vector. For alternatives whose characteristics are presented by vague sets, the proposed techniques can evaluate the degree of suitability to which an alternative satisfies the decision-maker＇ s requirement efficiently.

Characterization of Organic Aerosols in Beijing Using an Aerodyne High-Resolution Aerosol Mass Spectrometer

Fine particle of organic aerosol （OA）, mostly arising from pollution, are abundant in Beijing. To achieve a better un- derstanding of the difference in OA in summer and autumn, a high-resolution time-of-flight aerosol mass spectrometer （HR- ToF-AMS, Aerodyne Research Inc., USA） was deployed in urban Beijing in August and October 2012. The mean OA mass concentration in autumn was 30 4-30 μg m-3, which was higher than in summer （13 4-6.9 μg m-3）. The elemental anal- ysis found that OA was more aged in summer （oxygen-to-carbon （O/C） ratios were 0.41 and 0.32 for summer and autumn, respectively）. Positive matrix factorization （PMF） analysis identified three and five components in summer and autumn, re- spectively. In summer, an oxygenated OA （OOA）, a cooking-emission-related OA （COA）, and a hydrocarbon-like OA （HOA） were indentified. Meanwhile, the OOA was separated into LV-OOA （low-volatility OOA） and SV-OOA （semi-volatile OOA）; and in autumn, a nitrogen-containing OA （NOA） was also found. The SOA （secondary OA） was always the most important OA component, accounting for 55% of the OA in the two seasons. Back trajectory clustering analysis found that the origin of the air masses was more complex in summer. Southerly air masses in both seasons were associated with the highest OA loading, while northerly air masses were associated with the lowest OA loading. A preliminary study of OA components, especially the POA （primary OA）, in different periods found that the HOA and COA all decreased during the National Day holiday period, and HOA decreased at weekends compared with weekdays.

ON THE APPROXIMATE COMPUTATION OF EXTREME EIGENVALUES AND THE CONDITION NUMBER OF NONSINGULAR MATRICES

From the formulas of the conjugate gradient, a similarity between a symmetric positive definite (SPD) matrix A and a tridiagonal matrix B is obtained. The elements of the matrix B are determined by the parameters of the conjugate gradient. The computation of eigenvalues of A is then reduced to the case of the tridiagonal matrix B. The approximation of extreme eigenvalues of A can be obtained as a 'by-product' in the computation of the conjugate gradient if a computational cost of O(s) arithmetic operations is added, where s is the number of iterations This computational cost is negligible compared with the conjugate gradient. If the matrix A is not SPD, the approximation of the condition number of A can be obtained from the computation of the conjugate gradient on AT A. Numerical results show that this is a convenient and highly efficient method for computing extreme eigenvalues and the condition number of nonsingular matrices.

A combined statistical model for multiple motifs search

Transcription factor binding sites （TFBS） play key roles in genebior 6.8 wavelet expression and regulation. They are short sequence segments with definite structure and can be recognized by the corresponding transcription factors correctly. From the viewpoint of statistics, the candidates of TFBS should be quite different from the segments that are randomly combined together by nucleotide. This paper proposes a combined statistical model for finding over- represented short sequence segments in different kinds of data set. While the over-represented short sequence segment is described by position weight matrix, the nucleotide distribution at most sites of the segment should be far from the background nucleotide distribution. The central idea of this approach is to search for such kind of signals. This algorithm is tested on 3 data sets, including binding sites data set of cyclic AMP receptor protein in E.coli, PlantProm DB which is a non-redundant collection of proximal promoter sequences from different species, collection of the intergenic sequences of the whole genome of E.Coli. Even though the complexity of these three data sets is quite different, the results show that this model is rather general and sensible.

The realization of positive definite matrices via planar networks and mixing-type sub-cluster algebras

As an improvement of the combinatorial realization of totally positive matrices via the essential positive weightings of certain planar network by S.Fomin and A.Zelevinsky[7],in this paper,we give a test method of positive definite matrices via the planar networks and the so-called mixing-type sub-cluster algebras respectively,introduced here originally.This work firstly gives a combinatorial realization of all matrices through planar network,and then sets up a test method for positive definite matrices by LDU-decompositions and the horizontal weightings of all lines in their planar networks.On the other hand,mainly the relationship is built between positive definite matrices and mixing-type sub-cluster algebras.

Characteristics and source apportionment of ambient volatile organic compounds and ozone generation sensitivity in urban Jiaozuo,China

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.

Source apportionment and suitability evaluation of seasonal VOCs contaminants in the soil around a typical refining-chemical integration park in China

Accurate source apportionment of volatile organic compounds(VOCs)in soil nearby petrochemical industries prevailing globally,is critical for preventing pollution.However,in the process,seasonal effect on contamination pathways and accumulation of soil VOCs is often neglected.Herein,Yanshan Refining-Chemical Integration Park,including a carpet,refining,synthetic rubber,and two synthetic resin zones,was selected for traceability.Season variations resulted in a gradual decrease of 31 VOCs in soil from winter to summer.A method of dry deposition resistance model coupling partitioning coefficient model was created,revealing that dry deposition by gas phase was the primary pathway for VOCs to enter soil in winter and spring,with 100 times higher fux than by particle phase.Source profiles for five zones were built by gas sampling with distinct substance indicators screened,which were used for positive matrix factorization factors determination.Contributions of the five zones were 14.9%,20.8%,13.6%,22.1%,and 28.6%in winter and 33.4%,12.5%,10.7%,24.9%,and 18.5%in spring,respectively.The variation in the soil sorption capacity of VOCs causes inter-seasonal differences in contribution.The better correlation between dry deposition capacity and soil storage of VOCs made root mean square and mean absolute errors decrease averagely by 8.8%and 5.5%in winter compared to spring.This study provides new perspectives and methods for the source apportionment of soil VOCs contamination in industrial sites.

The Constrained Solutions of Two Matrix Equations

We study the symmetric positive semidefinite solution of the matrix equation AX_1A^T + BX_2B^T=C. where A is a given real m×n matrix. B is a given real m×p matrix, and C is a given real m×m matrix, with m, n, p positive integers: and the bisymmetric positive semidefinite solution of the matrix equation D^T XD=C, where D is a given real n×m matrix. C is a given real m×m matrix, with m. n positive integers. By making use of the generalized singular value decomposition, we derive general analytic formulae, and present necessary and sufficient conditions for guaranteeing the existence of these solutions.

LEAST-SQUARES SOLUTIONS OF X^TAX = B OVER POSITIVE SEMIDEFINITE MATRIXES A

This paper is mainly concerned with solving the following two problems: Problem Ⅰ. Given X ∈ Rn×m, B . Rm×m. Find A ∈ Pn such thatwhereProblem Ⅱ. Given A ∈Rn×n. Find A ∈ SE such thatwhere F is Frobenius norm, and SE denotes the solution set of Problem I.The general solution of Problem I has been given. It is proved that there exists a unique solution for Problem II. The expression of this solution for corresponding Problem II for some special case will be derived.

ON THE LEAST SQUARES PROBLEM OF A MATRIXEQUATION

Least squares solution of F=PG with respect to positive semidefinite symmetric P is considered,a new necessary and sufficient condition for solvablity is given,and the expression of solution is derived in the some special cases. Based on the expression, the least spuares solution of an inverse eigenvalue problem for positive semidefinite symmetric matrices is also given.

Chemical composition and source apportionment of PM2.5 and PM2.5-10 in Trombay （Mumbai, India）, a coastal industrial area

PM2.5 and PM2.5 lo concentrations, elemental constituents, and sources in a densely populated coastal industrial area （Trombay, Mumbai） were investigated in 2010 and 2011.The PM2.s and PM2.s lo concentra- tions were 13.50-71.60 and 22.40-127.78 p^g/m3, respectively. The daily PM25 concentrations exceeded the Indian Central Pollution Control Board limit （60 μg/m3） several days in winter. Of the elements analyzed, Si then Al had the highest concentrations in PM2.5- 10, but black carbon then Si had the highest concentrations in PM2.s. The element concentrations varied widely by season. Al, Ca, Fe, Si, and Ti con- centrations were highest in summer, Cl, Mg, and Na concentrations were highest in the monsoon season, and the other trace metal concentrations in both PM2.5 and PM2.5-10 were highest in winter. The PM2.5 and PM2.5-10 sources were apportioned by positive matrix factorization. PM2.5 and PM2.5-10 had six dominant sources, crustal material （8.7% and 25.3%, respectively）, sea salt spray （6.1% and 15.0%, respectively）, coal/biomass combustion （25.5% and 13.8%, respectively）, fuel oil combustion （19.0% and 11.2%, respectively）, road traffic （ 17.7% and 12.6%, respectively）, and the metal industry （ 10.6% and 7.0%, respectively）. Anthropogenic sources clearly contributed most to PM2.5 but natural sources contributed most to PM2.5-10.

Compositional metric learning for multi-label classification

Multi-label classification aims to assign a set of proper labels for each instance,where distance metric learning can help improve the generalization ability of instance-based multi-label classification models.Existing multi-label metric learning techniques work by utilizing pairwise constraints to enforce that examples with similar label assignments should have close distance in the embedded feature space.In this paper,a novel distance metric learning approach for multi-label classification is proposed by modeling structural interactions between instance space and label space.On one hand,compositional distance metric is employed which adopts the representation of a weighted sum of rank-1 PSD matrices based on com-ponent bases.On the other hand,compositional weights are optimized by exploiting triplet similarity constraints derived from both instance and label spaces.Due to the compositional nature of employed distance metric,the resulting problem admits quadratic programming formulation with linear optimization complexity w.r.t.the number of training examples.We also derive the generalization bound for the proposed approach based on algorithmic robustness analysis of the compositional metric.Extensive experiments on sixteen benchmark data sets clearly validate the usefulness of compositional metric in yielding effective distance metric for multi-label classification.