Water Column Chemical Dynamics Investigation via Multivariate Statistical Matrix Analysis to Evaluation of Hypoxia Potential Factors in Zarivar Lake

Phytoplankton is a sentinel group of organisms of climate change due to their capacity to respond to multiple stressors, so studies documenting the optimal optical conditions within the water column affecting their growth and production are imperative. As a contribution to this topic, this study report selected optical properties in deep-waters of the Gulf of Mexico by in situ measurements during summertime. A multidisciplinary research cruise was carried during August/September of 2018. A CTD instrument configured with underwater quantum and fluorescence sensors were used to acquire data of temperature, conductivity, depth, photosynthetically active radiation (PAR), and fluorescence of chlorophyll-a, which were used to determine selected optical coefficients, including the light extinction (k), the compensation light intensity (Ec), the compensation depth (Zc), the critical depth (Zcr), and the incident irradiance (E0). The Brunt-Väisälä frequency calculated from CTD data was used as a magnitude indicator of the water column stratification. The results showed a pycnocline located between 23 and 68 m depth, and favorable conditions for phytoplankton production with high values of E0 reaching 1523.4 μmol m-2 s-1, Ec values ranging from 3 to 8 μmol m-2 s-1, values of Zcr greater than Zc and maximum records of k values of 0.06. Based on multivariate statistical techniques, two zones were clearly defined. These results represent the first observational report on the optical properties in the deep region of the Gulf of Mexico. Studies on the ideal optical conditions for carrying out phytoplankton photosynthesis and their possible seasonal and interannual variability are essential to understand the processes that support the phytoplankton production, especially in regions that are characterized by their high biodiversity.

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Assessment of Spatiotemporal Variations in the Water Quality of the Han River Basin, South Korea, Using Multivariate Statistical and APCS-MLR Modeling Techniques

Agronomy ◽

10.3390/agronomy11122469 ◽

2021 ◽

Vol 11 (12) ◽

pp. 2469

Author(s):

Yong-Chul Cho ◽

Hyeonmi Choi ◽

Soon-Ju Yu ◽

Sang-Hun Kim ◽

Jong-Kwon Im

Keyword(s):

Water Quality ◽

Principal Component ◽

Domestic Sewage ◽

Pollution Sources ◽

Multivariate Statistical ◽

Han River ◽

Modeling Techniques ◽

Han River Basin ◽

Potential Factors ◽

Potential Pollution

This study evaluated the spatiotemporal variability of water quality in the Han River Basin (HRB) as well as the contributions of potential pollution sources using multivariate statistical and absolute principal component score-multiple linear regression (APCS-MLR) modeling techniques. From 2011 to 2020, data on water quality parameters were collected from 14 sites in the Ministry of Environment’s water quality monitoring network. Using spatiotemporal cluster analysis, these sites were classified into two periods over the year (dry and wet seasons) and into three regions: low pollution region (LPR), moderate pollution region (MPR), and high pollution region (HPR). Through principal component analysis, we identified four potential factors accounting for 80.1% and 74.1% of the total variance in the LPR and MPR, respectively, and three that accounted for 72.7% of the total variance in the HPR. APCS-MLR results indicated domestic sewage and phytoplankton growth (25%), domestic sewage and seasonal influence (29%), and point pollution sources caused by domestic sewage and industrial wastewater discharge (31%) as potential factors for the LPR, MPR, and HPR. These results demonstrate that the multivariate statistical techniques and the APCS-MLR model can be effectively used to monitor network design, quantitatively evaluate potential pollution sources, and establish efficient water quality management policies.

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Multivariate Statistical Methods Applied to Sizing and Fit Testing

Proceedings of the Human Factors and Ergonomics Society Annual Meeting ◽

10.1177/154193129804201015 ◽

1998 ◽

Vol 42 (10) ◽

pp. 732-735 ◽

Cited By ~ 1

Author(s):

Stacie E. Taylor

Keyword(s):

Statistical Methods ◽

Principal Component ◽

Distance Matrix ◽

Multivariate Regression Analysis ◽

Important Variable ◽

Matrix Analysis ◽

Multivariate Statistical Methods ◽

Euclidean Distance Matrix ◽

Multivariate Statistical ◽

Fit Testing

The difficulty of developing accurate sizing charts for clothing or equipment is often underestimated. Typically, designers intend for the item to fit a specific range of people. However, accommodation ofthat range is not always achieved. Fit testing is an important part of the design process that allows collection of data where the item is actually tried on and used by people, instead of mannequins. Multivariate statistical procedures are the proper analytic techniques for investigating this fit test data. Multivariate methods are used because univariate tests can cause designers to correct a “problem” fit area, leading to possibly more problems, instead of identifying important variable combinations which may be the true fit problem. Some of these multivariate statistical methods include principal component analysis (PCA), discriminant analysis (DA), Euclidean distance matrix analysis (EDMA), multivariate analysis of variance (MANOVA), and multivariate regression analysis (MRA). This paper discusses why and when to use these techniques and illustrates some of them with case studies.

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Long-term variations in individual particle types in the aerosol of Phoenix, Arizona, as determined using automated electron microprobe analysis and multivariate statistical techniques

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100149544 ◽

1993 ◽

Vol 51 ◽

pp. 742-743

Author(s):

Karen A. Katrinak ◽

James R. Anderson ◽

Peter R. Buseck

Keyword(s):

Electron Microprobe ◽

Fine Fraction ◽

Coarse Fraction ◽

Motor Vehicles ◽

Multivariate Statistical Methods ◽

Multivariate Statistical ◽

X Ray ◽

Mineral Particles ◽

Anthropogenic Air Pollutants

Aerosol samples were collected in Phoenix, Arizona on eleven dates between July 1989 and April 1990. Elemental compositions were determined for approximately 1000 particles per sample using an electron microprobe with an energy-dispersive x-ray spectrometer. Fine-fraction samples (particle cut size of 1 to 2 μm) were analyzed for each date; coarse-fraction samples were also analyzed for four of the dates.The data were reduced using multivariate statistical methods. Cluster analysis was first used to define 35 particle types. 81% of all fine-fraction particles and 84% of the coarse-fraction particles were assigned to these types, which include mineral, metal-rich, sulfur-rich, and salt categories. "Zero-count" particles, consisting entirely of elements lighter than Na, constitute an additional category and dominate the fine fraction, reflecting the importance of anthropogenic air pollutants such as those emitted by motor vehicles. Si- and Ca-rich mineral particles dominate the coarse fraction and are also numerous in the fine fraction.

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Angular Calibration of Ribosomal Subuntts in Factor Space

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100181063 ◽

1990 ◽

Vol 48 (1) ◽

pp. 462-463

Author(s):

Minakhi Pujari ◽

Joachim Frank

Keyword(s):

Three Dimensional ◽

Carbon Layer ◽

Uranyl Acetate ◽

Particle Analysis ◽

Factor Space ◽

Multivariate Statistical ◽

Ribosomal Subunits ◽

Layer Method ◽

Dimensional Reconstruction ◽

Number Of Particles

In single-particle analysis of macromolecule images with the electron microscope, variations of projections are often observed that can be attributed to the changes of the particle’s orientation on the specimen grid (“rocking”). In the multivariate statistical analysis (MSA) of such projections, a single factor is often found that expresses a large portion of these variations. Successful angle calibration of this “rocking factor” would mean that correct angles can be assigned to a large number of particles, thus facilitating three-dimensional reconstruction.In a study to explore angle calibration in factor space, we used 40S ribosomal subunits, which are known to rock around an axis approximately coincident with their long axis. We analyzed micrographs of a field of these particles, taken with 20° tilt and without tilt, using the standard methods of alignment and MSA. The specimen was prepared with the double carbon-layer method, using uranyl acetate for negative staining. In the MSA analysis, the untilted-particle projections were used as active, the tilted-particle projections as inactive objects. Upon tilting, those particles whose rocking axes are parallel to the tilt axis will change their appearance in the same way as under the influence of rocking. Therefore, each vector, in factor space, joining a tilted and untilted projection of the same particle can be regarded as a local 20-degree calibration bar.

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Molecular Views of Ice-Embedded Lumbricus Terrestris Erythrocruorin Obtained By Invariant Classification

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100181002 ◽

1990 ◽

Vol 48 (1) ◽

pp. 450-451

Author(s):

Michael schatz ◽

Joachim Jäger ◽

Marin van Heel

Keyword(s):

Image Interpretation ◽

Lumbricus Terrestris ◽

Reference Image ◽

Large Set ◽

Multivariate Statistical ◽

Data Set ◽

Low Contrast ◽

Negative Stain ◽

Reference Images ◽

Statistical Resolution

Lumbricus terrestris erythrocruorin is a giant oxygen-transporting macromolecule in the blood of the common earth worm (worm "hemoglobin"). In our current study, we use specimens (kindly provided by Drs W.E. Royer and W.A. Hendrickson) embedded in vitreous ice (1) to avoid artefacts encountered with the negative stain preparation technigue used in previous studies (2-4).Although the molecular structure is well preserved in vitreous ice, the low contrast and high noise level in the micrographs represent a serious problem in image interpretation. Moreover, the molecules can exhibit many different orientations relative to the object plane of the microscope in this type of preparation. Existing techniques of analysis requiring alignment of the molecular views relative to one or more reference images often thus yield unsatisfactory results.We use a new method in which first rotation-, translation- and mirror invariant functions (5) are derived from the large set of input images, which functions are subsequently classified automatically using multivariate statistical techniques (6). The different molecular views in the data set can therewith be found unbiasedly (5). Within each class, all images are aligned relative to that member of the class which contributes least to the classes′ internal variance (6). This reference image is thus the most typical member of the class. Finally the aligned images from each class are averaged resulting in molecular views with enhanced statistical resolution.

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