STUDY ON THE MULTIVARIATE STATISTICAL ESTIMATION OF TROPICAL CYCLONE INTENSITY USING FY-3 MWRI BRIGHTNESS TEMPERATURE DATA

A technique for estimating tropical cyclone(TC) intensity over the Western North Pacific utilizing FY-3Microwave Imager(MWRI) data is developed. As a first step, we investigated the relationship between the FY-3 MWRI brightness temperature(TB) parameters, which are computed in concentric circles or annuli of different radius in different MWRI frequencies, and the TC maximum wind speed(Vmax) from the TC best track data. We found that the parameters of lower frequency channels' minimum TB, mean TB and ratio of pixels over the threshold TB with a radius of 1.0 or 1.5 degrees from the center give higher correlation. Then by applying principal components analysis(PCA)and multiple regression method, we established an estimation model and evaluated it using independent verification data, with the RMSE being 13 kt. The estimated Vmax is always stronger in the early stages of development, but slightly weaker toward the mature stage, and a reversal of positive and negative bias takes place with a boundary of around 70 kt. For the TC that has a larger error, we found that they are often with less organized and asymmetric cloud pattern, so the classification of TC cloud pattern will help improve the acuracy of the estimated TC intensity, and with the increase of statistical samples the accuracy of the estimated TC intensity will also be improved.

Spatial variation assessment of groundwater quality using multivariate statistical analysis(Case Study:Fasa Plain,Iran)

Groundwater is considered as one of the most important sources for water supply in Iran.The Fasa Plain in Fars Province,Southern Iran is one of the major areas of wheat production using groundwater for irrigation.A large population also uses local groundwater for drinking purposes.Therefore,in this study,this plain was selected to assess the spatial variability of groundwater quality and also to identify main parameters affecting the water quality using multivariate statistical techniques such as Cluster Analysis(CA),Discriminant Analysis(DA),and Principal Component Analysis(PCA).Water quality data was monitored at 22 different wells,for five years(2009-2014)with 10 water quality parameters.By using cluster analysis,the sampling wells were grouped into two clusters with distinct water qualities at different locations.The Lasso Discriminant Analysis(LDA)technique was used to assess the spatial variability of water quality.Based on the results,all of the variables except sodium absorption ratio(SAR)are effective in the LDA model with all variables affording 92.80%correct assignation to discriminate between the clusters from the primary 10 variables.Principal component(PC)analysis and factor analysis reduced the complex data matrix into two main components,accounting for more than 95.93%of the total variance.The first PC contained the parameters of TH,Ca2+,and Mg2+.Therefore,the first dominant factor was hardness.In the second PC,Cl-,SAR,and Na+were the dominant parameters,which may indicate salinity.The originally acquired factors illustrate natural(existence of geological formations)and anthropogenic(improper disposal of domestic and agricultural wastes)factors which affect the groundwater quality.

New Method for Multivariate Statistical Process Monitoring

A new method using discriminant analysis and control charts is proposed for monitoring multivariate process operations more reliably.Fisher discriminant analysis （FDA） is used to derive a feature discriminant direction （FDD） between each normal and fault operations,and each FDD thus decided constructs the feature space of each fault operation.Individuals control charts （XmR charts） are used to monitor multivariate processes using the process data projected onto feature spaces.Upper control limit （UCL） and lower control limit （LCL） on each feature space from normal process operation are calculated for XmR charts,and are used to distinguish fault from normal.A variation trend on an XmR chart reveals the type of relevant fault operation.Applications to Tennessee Eastman simulation processes show that this proposed method can result in better monitoring performance than principal component analysis （PCA）-based methods and can better identify step type faults on XmR charts.

Joint multivariate statistical model and its applications to the synthetic earthquake prediction

Considering the problems that should be solved in the synthetic earthquake prediction at present, a new model is proposed in the paper. It is called joint multivariate statistical model combined by principal component analysis with discriminatory analysis. Principal component analysis and discriminatory analysis are very important theories in multivariate statistical analysis that has developed quickly in the late thirty years. By means of maximization information method, we choose several earthquake prediction factors whose cumulative proportions of total sam-ple variances are beyond 90% from numerous earthquake prediction factors. The paper applies regression analysis and Mahalanobis discrimination to extrapolating synthetic prediction. Furthermore, we use this model to charac-terize and predict earthquakes in North China (30~42N, 108~125E) and better prediction results are obtained.

Quality assessment of Jinhongtang Granule using UFLC-MS/MS and multivariate statistical analysis

Jinhongtang is a traditional Chinese medicine formula composed of Rheum palmatum L.stem,Sargentodoxa cuneata stem,and Taraxacum mongolicum and is used for the treatment of sepsis.However,quality assessment method for Jinhongtang is not available.In present study,we developed a UFLC-MS/MS method to determine 16 analytes in 20 batches of home-made and commercial Jinhongtang.Multivariate statistical analysis revealed the significant differences in the quality of home-made and commercial Jinhongtang and the difference in the quality of home-made samples was more significant.The integrated strategy based on UFLC-MS/MS and multivariate statistical analysis provided a new basis for the overall quality assessment of Jinhongtang.

Study on the Relationship between Soil and Environment Based on Multivariate Statistical Analysis

[Objective] The study aimed to study the relationship between soil and environment on the basis of multivariate statistical analysis. [ Method] Through field investigation, sampling and laboratory analysis, we discussed the relationship between soil properties and environmental factors in Mizhi County, North Shaanxi by using Canoco multivariate statistical analysis. [ Result]According to the effects of various environmental factors on soil properties, the influencing order of environmental factors was land use way 〉 vegetation type 〉 vegetation restoration years 〉 vegeta- tion coverage 〉 slope aspect 〉 gradient 〉 elevation. In a word, soil properties were significantly affected by land use way and vegetation type which were the most important environmental factors of soil properties in spatial variation, while vegetation restoration years were closely related to the ac- cumulation of soil nutrients. [ Condusion]The research could provide theoretical references for the construction of ecological environment in Loess Plateau of China.

Multivariate Statistical Analysis of Large Datasets: Single Particle Electron Microscopy

Biology is a challenging and complicated mess. Understanding this challenging complexity is the realm of the biological sciences: Trying to make sense of the massive, messy data in terms of discovering patterns and revealing its underlying general rules. Among the most powerful mathematical tools for organizing and helping to structure complex, heterogeneous and noisy data are the tools provided by multivariate statistical analysis (MSA) approaches. These eigenvector/eigenvalue data-compression approaches were first introduced to electron microscopy (EM) in 1980 to help sort out different views of macromolecules in a micrograph. After 35 years of continuous use and developments, new MSA applications are still being proposed regularly. The speed of computing has increased dramatically in the decades since their first use in electron microscopy. However, we have also seen a possibly even more rapid increase in the size and complexity of the EM data sets to be studied. MSA computations had thus become a very serious bottleneck limiting its general use. The parallelization of our programs—speeding up the process by orders of magnitude—has opened whole new avenues of research. The speed of the automatic classification in the compressed eigenvector space had also become a bottleneck which needed to be removed. In this paper we explain the basic principles of multivariate statistical eigenvector-eigenvalue data compression;we provide practical tips and application examples for those working in structural biology, and we provide the more experienced researcher in this and other fields with the formulas associated with these powerful MSA approaches.

Water Quality Assessment of a Tropical Mexican Lake Using Multivariate Statistical Techniques

Water quality of Mexican tropical lake Chapala was assessed through multivariate statistical techniques, cluster analysis (CA) and principal component analysis (PCA) at ten different monitoring sites for ten physicochemical variables and six metals. This study evaluated and interpreted complex water quality data sets and apportioned of pollution sources to get better information about water quality. From descriptive statistics results, the highest concentrations of metals occurred during the dry season, and this trend was explained by the fact that an unusual rainy event occurred during the month of February 2009 and brought metals into the lake by runoffs from nearby mountains. According to international criteria for water consumption by aquatic organisms [USEPA], only Zn concentration values were below these criteria whereas the values of Ni, Pb, Cd and Fe were above the corresponding values set in these criteria (Ni: 52 μg·L-1, Pb: 2.5 μg·L-1, Cd: 0.25 μg·L-1, and Fe: 1000 μg·L-1). The correlations were observed by PCA, which were used to classify the samples by CA, based on the PCA scores. Seven significant cluster groups of sampling locations—(sites 4 and 5), (sites 3 and 9), (site 7), (site 10), (sites 2 and 6), (site 8) and (site 1)— were detected on the basis of similarity of their water quality. The results revealed that the stress exerted on the lake caused by waste sources follows the order: domestic > agricultural > industrial.

Using Multivariate Statistical and Geostatistical Methods to Identify Spatial Variability of Trace Elements in Agricultural Soils in Dongguan City,Guangdong,China

Dongguan （东莞） City, located in the Pearl River Delta, South China, is famous for its rapid industrialization in the past 30 years. A total of 90 topsoil samples have been collected from agricultural fields, including vegetable and orchard soils in the city, and eight heavy metals （As, Cu, Cd, Cr, Hg, Ni, Pb, and Zn） and other items （pH values and organic matter） have been analyzed, to evaluate the influence of anthropic activities on the environmental quality of agricultural soils and to identify the spatial distribution of trace elements and possible sources of trace elements. The elements Hg, Pb, and Cd have accumulated remarkably here, incomparison with the soil background content of elements in Guangdong （广东） Province. Pollution is more serious in the western plain and the central region, which are heavily distributed with industries and rivers. Multivariate and geostatistical methods have been applied to differentiate the influences of natural processes and human activities on the pollution of heavy metals in topsoils in the study area. The results of cluster analysis （CA） and factor analysis （FA） show that Ni, Cr, Cu, Zn, and As are grouped in factor F1, Pb in F2, and Cd and Hg in F3, respectively. The spatial pattern of the three factors may be well demonstrated by geostatistical analysis. It is shown that the first factor could be considered as a natural source controlled by parent rocks. The second factor could be referred to as ＂industrial and traffic pollution sources＂. The source of the third factor is mainly controlled by long-term anthropic activities, as a consequence of agricultural activities, fossil fuel consumption, and atmospheric deposition.

Rapid Analysis and Identification of Absorbed Components and Their Metabolites of Yuanhu Zhitong Dropping Pill in Rat Plasma and Brain Tissue Using UPLC-Q-TOF/MS with Multivariate Statistical Analysis

Objective To establish an effective approach for rapid and comprehensive analysis on the absorbed and metabolic components in rats after ig administration of Yuanhu Zhitong Dropping Pill（YHZT）. Methods Based on the combination of UPLC-Q-TOF/MS and multivariate statistical analysis, the absorbed prototype constituents and their metabolites in rat plasma were rapidly analyzed and identified, and the components absorbed into brain were further identified by comparing the extracted ion chromatograms（EICs） of control and brain tissue samples of dosed rats. Results A total of 38 YHZT-related xenobiotic compounds were detected and identified as the potential bioactive constituents in rat plasma, including 24 absorbed prototype constituents and 14 metabolites. In particular, of all prototype constituents, 14 were also detected in rat brain tissue, indicating that they could penetrate the blood-brain barrier and enter into brain. Conclusion An effective method is established and applied to analyze the potential bioactive constituents in YHZT, which provides a pathway to further investigate the pharmacological pattern and mechanism of YHZT.

Chemical comparison of dried rehmannia root and prepared rehmannia root by UPLC-TOF MS and HPLC-ELSD with multivariate statistical analysis

To identify the chemical differences which lead to the different therapeutic effects of dried rehmannia root(DRR)and prepared rehmannia root(PRR),we compared the chemical composition of decoctions of randomly purchased DRR and PRR using ultra performance liquid chromatography(UPLC)coupled with time-of-fight mass spectrometry and high performance liquid chromatography(HPLC)coupled with evaporative light scattering detection(ELSD)with the aid of multivariate statistical analysis.Both approaches clearly revealed compositional and quantitative differences between DRR and PRR.UPLC-MS data indicated stachyose,rehmaiono-side A(or rehmaionoside B),acteoside(or forsythiaside,or isoacteoside),6-O-coumaroylajugol(or 6-O-E-feruloylajugol,or 6-O-Z-feruloylajugol)as important discriminators between DRR and PRR decoctions.HPLC-ELSD analysis showed that the content of fructose in the decoctions of PRR was about four times greater than that of DRR(P<10^(-5)),while sucrose content in the decoctions of PRR was only about one seventh of that in DRR(P<0.01).Our results suggest that some compounds,such as fructose,stachyose and rehmaionoside,may be responsible for the differing therapeutic effects of DRR and PRR.Furthermore,improvements in quality control for PRR,which is currently lacking in the Chinese Pharmacopoeia,are recommended.

Combined Use of Multivariate Statistical Analysis and Hydrochemical Analysis for Groundwater Quality Evolution: A Case Study in North Chain Plain

Understanding the controlling factor of groundwater quality can enhance promoting sustainable development of groundwater resources. To this end, multivariate statistical analysis（MA） and hydrochemical analysis were introduced in this work. The results indicate that the canonical discriminant function with 7 parameters was established using the discriminant analysis（DA） method, which can afford 100% correct assignation according to the 3 different clusters（good water（GW）, poor water（PW）, and very poor water（VPW）） obtained from cluster analysis（CA）. According to factor analysis（FA）, 8 factors were extracted from 25 hydrochemical elements and account for 80.897% of the total data variance, suggesting that groundwater with higher concentrations of sodium, calcium, magnesium, chloride, and sulfate in southeastern study area are mainly affected by the natural process; the higher level of arsenic and chromium in groundwater extracted from northwestern part of study area are derived by industrial activities; domestic and agriculture sewage have important contribution to copper, iron, iodine, and phosphate in the northern study area. Therefore, this work can help identify the main controlling factor of groundwater quality in North China plain so as to make better and more informed decisions about how to achieve groundwater resources sustainable development.

Chemical Characteristics Combined with Bioactivity for Comprehensive Evaluation of Tumuxiang Based on HPLC-DAD and Multivariate Statistical Methods

Background: The dried roots of Inula helenium L.(IH) and Inula racemosa Hook f.(IR) are used commonly as folk medicine under the name of "tumuxiang(TMX)". Phenolic acid compounds and their derivatives, as main active constituents in IH and IR, exhibit prominent anti-inflammation effect.Objective: To develop a holistic method based on chemical characteristic and anti-inflammation effect for systematically evaluating the quality of twenty-seven TMX samples(including 18 IH samples and 9 IR samples) from different origins.Methods: HPLC fingerprints data of AL(Aucklandia lappa Decne.) whose dried root was similar with HR was added for classification analysis. The HPLC fingerprints of twenty-seven TMX samples and four AL samples were evaluated using hierarchical clustering analysis(HCA) and principle component analysis(PCA). The spectrum-efficacy model between HPLC fingerprints and anti-inflammatory activities was investigated by principal component regression(PCR) and partial least squares(PLS).Results: All samples were successfully divided into three main clusters and peaks 7, 9, 11, 22, 24 and 26 had a primary contribution to classify these medicinal herbs. The results were in accord with the appraisal results of herbs. The spectrum-efficacy relationship results indicated that citric acid, quinic acid, caffeic acid-β-D-glucopyranoside, chlorogenic acid, caffeic acid, 1,3-O-dicaffeoyl quinic acid, tianshic acid and 3β-Hydroxypterondontic acid had main contribution to anti-inflammatory activities.Conclusion: This comprehensive strategy was successfully used for identification of IH, IR and AL, which provided a reliable and adequate theoretical basis for the bioactivity relevant quality standards and studying the material basis of anti-inflammatory effect of TMX.

Machining Error Control by Integrating Multivariate Statistical Process Control and Stream of Variations Methodology

For aircraft manufacturing industries, the analyses and prediction of part machining error during machining process are very important to control and improve part machining quality. In order to effectively control machining error, the method of integrating multivariate statistical process control （MSPC） and stream of variations （SoV） is proposed. Firstly, machining error is modeled by multi-operation approaches for part machining process. SoV is adopted to establish the mathematic model of the relationship between the error of upstream operations and the error of downstream operations. Here error sources not only include the influence of upstream operations but also include many of other error sources. The standard model and the predicted model about SoV are built respectively by whether the operation is done or not to satisfy different requests during part machining process. Secondly, the method of one-step ahead forecast error （OSFE） is used to eliminate autocorrelativity of the sample data from the SoV model, and the T2 control chart in MSPC is built to realize machining error detection according to the data characteristics of the above error model, which can judge whether the operation is out of control or not. If it is, then feedback is sent to the operations. The error model is modified by adjusting the operation out of control, and continually it is used to monitor operations. Finally, a machining instance containing two operations demonstrates the effectiveness of the machining error control method presented in this paper.

A quick and effective multivariate statistical strategy for imaging mass spectrometry

A new multivariate statistical strategy for analyzing large datasets that are produced by imaging mass spectrometry（IMS） techniques is reported.The strategy divides the whole datacube of the sample into several subsets and analyses them one by one to obtain the results.Instead of analyzing the whole datacube at one time,the strategy makes the analysis easier and decreases the computation time greatly.In this report,the IMS data are produced by the air flow-assisted ionization IMS（AFAI-IMS）.The strategy can be used in combination with most multivariate statistical analysis methods.In this paper,the strategy was combined with the principal component analysis（PCA） and partial least square analysis（PLS）.It was proven to be effective by analyzing the handwriting sample.By using the strategy,the m/z corresponding to the specific lipids in rat brain tissue were distinguished successfully.Moreover the analysis time grew linearly instead of exponentially as the size of sample increased.The strategy developed in this study has enormous potential for searching for the mjz of potential biomarkers quickly and effectively.

Assessment of spatial and seasonal water quality variation of Oum Er Rbia River(Morocco)using multivariate statistical techniques

The aim of this study is to assess the spatial and temporal water quality variation and to determine the main contamination sources in the Oum Er Rbia River and its main tributary,El Abid River.The water quality data were collected during 2000-2012 from fourteen sampling stations distributed along the river.The water quality indicators used were TEMP,pH,EC,turbidity,TSS,DO,NH_(4)^(+),NH_(3)^(-),TP,BOD5,COD and F.coli.The water quality data was analyzed using multivariate statistical methods including Pearson's correlation,PCA,and CA.The results showed that in some stations the water quality parameters were over Moroccan water standards.PCA applied to compare the compositional patterns among the analyzed water samples,identified and four factors accounting for almost 63% of the total variation in the data.This suggests that the variations in water compounds’concentration are mainly related to point source contamination(domestic and industrial wastewater),non-point source contamination(agriculture activities),as well as natural processes(weathering of soil and rock).CA showed relatively spatial and seasonal changes in surface water quality,which are usually indicators of contamination with rainfalls or other sources.Overall,this study showed that the water was potentially hazardous to health of the consumers and highlighted the need to treat industrial and municipal wastewater and to encourage sustainable agricultural practices to prevent adverse health effects.We therefore suggest wise management of anthropogenic activities in the catchment of Oum Er Bia River and their tributaries.

Assessment of temporal and spatial variations in water quality using multivariate statistical methods： a case study of the Xin＇anjiang River, China

This study evaluated the temporal and spatial variations of water quality data sets for the Xin＇anjiang River through the use of multivariate statistical techniques, including cluster analysis （CA）, discriminant analysis （DA）, correlation analysis, and principal component analysis （PCA）. The water samples, measured by ten parameters, were collected every month for three years （2008-2010） from eight sampling stations located along the river. The hierarchical CA classified the 12 months into three periods （First, Second and Third Period） and the eight sampling sites into three groups （Groups 1, 2 and 3） based on seasonal differences and various pollution levels caused by physicochemical properties and anthropogenic activ- ities. DA identified three significant parameters （tempera- ture, pH and E.coli） to distinguish temporal groups with close to 76% correct assignment. The DA also discovered five parameters （temperature, electricity conductivity, total nitrogen, chemical oxygen demand and total phosphorus） for spatial variation analysis, with 80.56% correct assignment. The non-parametric correlation coefficient （Spear- man R） explained the relationship between the water quality parameters and the basin characteristics, and the GIS made the results visual and direct. The PCA identified four PCs for Groups 1 and 2, and three PCs for Group 3. These PCs captured 68.94%, 67.48% and 70.35% of the total variance of Groups 1, 2 and 3, respectively. Although natural pollution affects the Xin＇anjiang River, the main sources of pollution included agricultural activities, industrial waste, and domestic wastewater.

Characteristics and genesis of groundwater salinization in coastal areas of the Lower Reaches of Oujiang Basin

The coastal areas of the lower reaches of Oujiang River Basin are rich in groundwater resources.However,the unsustainable exploitation and utilization of groundwater have led to significant changes in the groundwater environment.Understanding the characteristics and genesis of groundwater salinization is crucial for preventing its deterioration and ensuring sustainable utilization.In this study,a comprehensive approach combining the ion ratio method,mineral saturation index method and multivariate statistical analysis was employed to investigate the hydrochemical characteristics and main controlling factors in the study area.The findings reveal that:(1)Groundwater samples in study area exhibit a neutral to slightly alkaline pH.The predominant chemical types of unconfined water are HCO_(3)-Ca·Na,HCO_(3)·Cl-Na·Ca and HCO_(3)·SO_(4)-Ca·Na,while confined water mainly exhibits Cl·HCO_(3)-Na and Cl-Na types.(2)Salinity coefficients indicate an increase in salinity from unconfined to confined water.TDS,Na^(+)and Cl^(–)concentrations show an increasing trend from mountainous to coastal areas in unconfined water,while confined water displays variability in TDS,Na^(+)and Cl^(–)concentrations.(3)Groundwater salinity is mainly influenced by water-rock interactions,including the dissolution of halite and gypsum,cation exchange,and seawater intrusion etc.Additionally,human activities and carbonate dissolution contribute to salinity in unconfined water.Seawater intrusion is identified as the primary factor leading to higher salinity in confined water compared to unconfined water,with increasing cation exchange and seawater interaction observed from unconfined to confined water.

Statistical evaluation of fluoride contamination in groundwater resources of Santiago del Estero Province, Argentina

This study investigates the suitability of statistical techniques for evaluating the fluoride content and the groundwater quality from Robles Department(RD)and Banda Department(BD)in Santiago del Estero(Argentina).For the original statistical study,evaluation of nine parameters(fluoride,pH,conductivity,atmospheric and water temperature,total dissolved solids,chloride,hardness,and alkalinity)of 110 collected underground water samples from 23 dispersed rural areas was proposed.Groundwater samples were obtained by sampling taken from wells at different depths.Fluoride levels were determined by a standard colorimetric method in two seasonal periods,the dry(from April to September)and rainy(from October to March)period.The analytical results obtained for physicochemical parameters such as pH,total dissolved solids(TDS),and temperature does not reveal any notable difference between the rainy and dry seasons studied.In both seasons,the atmospheric temperature average was 22℃.With respect to fluoride content,approximately 50%of the analysed groundwater samples exceeded the limit established by current legislation(1.0 mg/L),obtaining concentration levels in the range of 0.01-2.80 mg/L.This study demonstrates the usefulness of the univariate statistical method(quartiles calculation,interquartile range IQR),multivariate principal component analysis(PCA),and cluster analysis to establish a better understanding of the state of the contamination of the waters in the region studied.

Multivariate analysis and geochemical investigations of groundwater in a semi-arid region, case of superficial aquifer in Ghriss Basin, Northwest Algeria

This study aims to investigate the hydrochemical characteristics of shallow aquifer in a semi-arid region situated in northwest Algeria,and to understand the major factors governing groundwater quality.The study area is suffering from recurring droughts,groundwater resource over-exploitation and groundwater quality degradation.The approach used is a combination of traditional hydrochemical analysis methods of multivariate statistical techniques,principal component analysis(PCA),and ratios of major ions,based on the data derived from 33 groundwater samples collected in February 2014.Results show that groundwater in the study area are highly mineralized and collectively has a high concentration of chloride(as Cl^(−)).The dominant water types are Na-Cl(27%),Mg-HCO_(3)(24%)and Mg-Cl(24%).According to the(PCA)approach,salinization is the main process that controls the hydrochemical variability.The PCA analysis reveal the impact of anthropogenic factor especially the agricultural activities on the groundwater quality.The PCA highlighted two types of recharge:Superficial recharge from effective rainfall and excess irrigation water distinguished by the presence of nitrate and lateral recharge or vertical leakage from carbonate formations marked by the omnipresence of HCO_(3)^(−).Additionally,three categories of samples were identified:(1)samples characterized by good water quality and receiving notable recharge from carbonate formations;(2)samples impacted by the natural salinization process;and(3)samples contaminated by anthropogenic activities.The major natural processes influencing water chemistry are the weathering of carbonate and silicate rocks,dissolution of evaporite as halite,evaporation and cation exchange.The study results can provide the basis for local decision makers to ensure the sustainable management of groundwater and the safety of drinking water.