Mathematical Models for Analysis of Water Pollution

2012 ◽  
Vol 209-211 ◽  
pp. 1941-1947
Author(s):  
Xuan Hua Qin ◽  
Li Li Zheng
Keyword(s):  
Water Pollution ◽  
Water Quality ◽  
Principal Component Analysis ◽  
Regression Analysis ◽  
Mathematical Models ◽  
Pollution Index ◽  
Principal Component ◽  
Pollution Factor ◽  
Main Components ◽  
The Relationship

The water qualityr was considered by multiple regression analysis. The linear relationship between integrated pollution index and weight pollution index were obtained, the 5 day biochemical oxygen demanded and total nitrogen were the most important pollution factor by the relationship. And then, the two main components influencing water quality were given based on the principal component analysis affecting data..

The Application of Principal Component Analysis in Comprehensive Evaluation of Liaohe River Water Quality

2013 ◽  
Vol 779-780 ◽  
pp. 1596-1599 ◽  
Author(s):  
Lei Zhu ◽  
Ming Li
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Cross Section ◽  
Water Environment ◽  
Principal Component ◽  
Quality Monitoring ◽  
Water Quality Indicators ◽  
Liaohe River ◽  
Environment Quality ◽  
The Relationship

In order to explore the relationship between water quality indicators and the cross-section,this paper applied principal component analysis to evaluate comprehensively the water quality monitoring sections of the Liaohe River. The results showed that the water quality of the Liaohe River Tieling and Shenyang segments belonged to the inferior class V water. In this paper, water quality assessment and water environment quality grading used respectively two ways that were not and setting up a virtual cross-section to avoid the classification of water environment quality impacting on the relationship between water quality indicators and quality monitoring sections. Principal component score applied two and three-dimensional graphic display,made expression of the water pollution situation more intuitive.

scholarly journals Daily water quality evaluation of reservoir and cyanobacteria pollution index calculation

2020 ◽  
Author(s):  
Wen Zhang ◽  
Zhiwei Liu
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Quality Evaluation ◽  
Pollution Index ◽  
Principal Component ◽  
Component Analysis ◽  
Biological Factor ◽  
Water Quality Evaluation ◽  
Factor Evaluation ◽  
Index Calculation

Abstract Water quality evaluation is the most direct and quantitative description of reservoir water environment. In view of the lack of biological factor evaluation in water quality evaluation at present, firstly the conventional pollution index evaluation system was used to evaluate the water quality continuously. Then the correlation between the evaluation indexes and the concentration of cyanobacteria was studied. After determining the correlation, a novel calculation method of cyanobacteria pollution index was determined by principal component analysis (PCA). The result showed that the eutrophication index and nitrogen phosphorus index of the water body were high. Biological pollution in reservoir cannot be ignored. The correlation between eutrophication index and cyanobacteria concentration only reached a weak correlation (Pearson correlation = 0.242). For the reservoir, it is necessary to establish a special cyanobacteria pollution index. Five variables participated cyanobacteria pollution index calculation by principal component analysis (PCA) method. The total variance of the two main components was 77.107%, which can reflect most of the data information. In the reservoirs of other areas, similar parameters can also be selected to calculate the cyanobacteria pollution index. This research provide reference for the biological factor evaluation of similar reservoirs in the world.

scholarly journals Remote Sensing Calibration of Casey Lake and Silver Lake

2005 ◽  
Vol 4 (1) ◽  
Author(s):  
Krista Ellyson ◽  
Mark Ecker
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Principal Component Analysis ◽  
Regression Analysis ◽  
Total Phosphorus ◽  
Principal Component ◽  
Component Analysis ◽  
Phosphorus Levels ◽  
Silver Lake

This project examines two Iowa lakes to explore the feasibility of using remote sensing technologies for assessing water quality in lieu of actual ground samples. We demonstrate that a principal component analysis of the more than 20,000 remote sensed pixels can be used in a regression analysis to accurately predict total phosphorus levels in Casey Lake on three distinct times in the summer of 2004.

scholarly journals La influencia del desarrollo moral de los adolescentes en su comportamiento antisocial autorreportado: El caso de las adolescentes (The influence of moral reasoning of adolescents in their self-reported antisocial behaviour: The case of girls)

2020 ◽  
Vol 10 (2) ◽  
pp. 489-512
Author(s):  
María José Bernuz Beneitez ◽  
María A. González-Álvarez
Keyword(s):  
Public Health ◽  
Principal Component Analysis ◽  
Regression Analysis ◽  
Moral Development ◽  
Moral Reasoning ◽  
Antisocial Behavior ◽  
Principal Component ◽  
Antisocial Behaviour ◽  
Different Dimensions ◽  
The Relationship

El objetivo del presente artículo es analizar la relación entre el desarrollo moral de los adolescentes y sus comportamientos delictivos (auto-reportados) distinguiendo según si son conductas contra la propiedad, las personas, el orden, o la salud pública. Se utilizan datos de una encuesta realizada en 2012 en Aragón a 1.762 adolescentes entre 14 y 18 años. El estudio emplea el análisis de componentes principales para identificar la estructura interna de los datos de algunas de las distintas dimensiones del razonamiento moral de los adolescentes. El análisis de regresión múltiple muestra que una mayor penalización de conductas antisociales, tener a los adultos como figura de referencia y ser chica reducen la probabilidad de cometer un delito. Mientras que la (mayor) edad, la legitimación de la violencia y la sumisión y optar por mecanismos de prevención social incrementan significativamente el riesgo de delinquir. The aim of this paper is to analyze the relationship between moral development of adolescents and their antisocial behavior (self-reported) differentiating on the basis of the nature of the conduct, if it is against property, against people, against the order, or against public health. The data used comes from a survey conducted in 2012 in Aragon to 1,762 adolescents between 14 and 18 years of age. Principal component analysis is used to identify the internal structure of the data for the different dimensions of moral reasoning of adolescents (reasoning about the rightfulness of anti-social behaviors, tolerance towards violence, opinion about prevention mechanisms, and their figures of reference). The regression analysis shows that higher penalty of antisocial behavior, having adults as leading figures and being a girl reduce the probability of committing a crime. On the contrary, (higher) age, tolerance towards violence and submission, and a greater preference for social prevention mechanisms significantly increase the risk of being an offender.

scholarly journals Hubungan Kelimpahan dan Keanekaragaman Fitoplankton dengan Kelimpahan dan Keanekaragaman Zooplankton di Perairan Pulau Serangan, Bali

2018 ◽  
Vol 4 (2) ◽  
pp. 324
Author(s):  
Luh Putu Puspita Dewanti ◽  
I Dewa Nyoman Nurweda Putra ◽  
Elok Faiqoh
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Correlation Analysis ◽  
Human Activities ◽  
Pearson Correlation ◽  
Principal Component ◽  
Phytoplankton Abundance ◽  
Pearson Correlation Analysis ◽  
Abundance And Diversity ◽  
The Relationship

Plankton is one of the biological resources that have an important role in the marine ecosystem. Plankton life is strongly influenced by the water quality parameters, one of which is the content of nutrients (nitrates and phosphates). Increased nutrient content caused by the increased load input from human activities. Serangan Island waters including the coastal ecosystem is widely used for a variety of human activities, such as tourism, aquaculture, residential, and transportation. All human activity will affect water quality will lead to an increase in nutrients and organic matter which in turn can lead to changes in water quality chemical physics and structure of plankton. The purpose of this study was to determine the relationship of the abundance and diversity of phytoplankton abundance and diversity of zooplankton and to know the physical parameters - chemical effect on the abundance of plankton. The method used is the Pearson correlation analysis to determine the relationship between abundance and diversity of phytoplankton abundance and diversity of zooplankton, and principal component analysis to look at the parameters of the water the most influence on the abundance of plankton. Results of Principal Component Analysis showed that the waters of the parameters that influence the abundance of plankton varies at each observation station. Pearson correlation analysis showed a strong relationship between the abundance of phytoplankton to zooplankton abundance with a correlation value of 0.64.

scholarly journals Quantify Coal Macrolithotypes of a Whole Coal Seam: A Method Combing Multiple Geophysical Logging and Principal Component Analysis

Energies ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 213
Author(s):  
Chao Cui ◽  
Suoliang Chang ◽  
Yanbin Yao ◽  
Lutong Cao
Keyword(s):  
Principal Component Analysis ◽  
Coal Seam ◽  
Coalbed Methane ◽  
Principal Component ◽  
Component Analysis ◽  
Gas Production ◽  
Index Model ◽  
Geophysical Logging ◽  
The Relationship ◽  
Observation Results

Coal macrolithotypes control the reservoir heterogeneity, which plays a significant role in the exploration and development of coalbed methane. Traditional methods for coal macrolithotype evaluation often rely on core observation, but these techniques are non-economical and insufficient. The geophysical logging data are easily available for coalbed methane exploration; thus, it is necessary to find a relationship between core observation results and wireline logging data, and then to provide a new method to quantify coal macrolithotypes of a whole coal seam. In this study, we propose a L-Index model by combing the multiple geophysical logging data with principal component analysis, and we use the L-Index model to quantitatively evaluate the vertical and regional distributions of the macrolithotypes of No. 3 coal seam in Zhengzhuang field, southern Qinshui basin. Moreover, we also proposed a S-Index model to quantitatively evaluate the general brightness of a whole coal seam: the increase of the S-Index from 1 to 3.7, indicates decreasing brightness, i.e., from bright coal to dull coal. Finally, we discussed the relationship between S-Index and the hydro-fracturing effect. It was found that the coal seam with low S-Index values can easily form long extending fractures during hydraulic fracturing. Therefore, the lower S-Index values indicate much more favorable gas production potential in the Zhengzhuang field. This study provides a new methodology to evaluate coal macrolithotypes by using geophysical logging data.

scholarly journals Long-Term River Water Quality Trends and Pollution Source Apportionment in Taiwan

Water ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 1394 ◽  
Author(s):  
Marsha Putri ◽  
Chao-Hsun Lou ◽  
Mat Syai’in ◽  
Shang-Hsin Ou ◽  
Yu-Chun Wang
Keyword(s):  
Water Pollution ◽  
Water Quality ◽  
Heavy Metal ◽  
Cluster Analysis ◽  
River Water ◽  
Principal Component ◽  
Pollution Level ◽  
Metal Levels ◽  
Metal Monitoring

The application of multivariate statistical techniques including cluster analysis and principal component analysis-multiple linear regression (PCA-MLR) was successfully used to classify the river pollution level in Taiwan and identify possible pollution sources. Water quality and heavy metal monitoring data from the Taiwan Environmental Protection Administration (EPA) was evaluated for 14 major rivers in four regions of Taiwan with the Erren River classified as the most polluted river in the country. Biochemical oxygen demand (6.1 ± 2.38), ammonia (3.48 ± 3.23), and total phosphate (0.65 ± 0.38) mg/L concentration in this river was the highest of the 14 rivers evaluated. In addition, heavy metal levels in the following rivers exceeded the Taiwan EPA standard limit (lead: 0.01, copper: 0.03, and manganese: 0.03) mg/L concentration: lead-in the Dongshan (0.02 ± 0.09), Jhuoshuei (0.03 ± 0.03), and Xinhuwei Rivers (0.02 ± 0.02) mg/L; copper: in the Dahan (0.036 ± 0.097), Laojie (0.06 ± 1.77), and Erren Rivers are (0.05 ± 0.158) mg/L; manganese: in all rivers. A total 72% of the water pollution in the Erren River was estimated to originate from industrial sources, 16% from domestic black water, and 12% from natural sources and runoff from other tributaries. Our research demonstrated that applying PCA-MLR and cluster analysis on long-term monitoring water quality would provide integrated information for river water pollution management and future policy making.

scholarly journals Air Pollution and Human Development in Europe: A New Index Using Principal Component Analysis

2018 ◽  
Vol 10 (2) ◽  
pp. 312 ◽  
Author(s):  
Ana-Maria Săndică ◽  
Monica Dudian ◽  
Aurelia Ştefănescu
Keyword(s):  
Principal Component Analysis ◽  
Human Development ◽  
Quantitative Measure ◽  
Principal Component ◽  
Component Analysis ◽  
Life Expectancy At Birth ◽  
Real Gdp ◽  
Development Indicator ◽  
The Relationship ◽  
Pm2.5 Concentration

EU countries to measure human development incorporating the ambient PM2.5 concentration effect. Using a principal component analysis, we extract the information for 2010 and 2015 using the Real GDP/capita, the life expectancy at birth, tertiary educational attainment, ambient PM2.5 concentration, and the death rate due to exposure to ambient PM2.5 concentration for 29 European countries. This paper has two main results: it gives an overview about the relationship between human development and ambient PM2.5 concentration, and second, it provides a new quantitative measure, PHDI, which reshapes the concept of human development and the exposure to ambient PM2.5 concentration. Using rating classes, we defined thresholds for both HDI and PHDI values to group the countries in four categories. When comparing the migration matrix from 2010 to 2015 for HDI values, some countries improved the development indicator (Romania, Poland, Malta, Estonia, Cyprus), while no downgrades were observed. When comparing the transition matrix using the newly developed indicator, PHDI, the upgrades observed were for Denmark and Estonia, while some countries like Spain and Italy moved to a lower rating class due to ambient PM2.5 concentration.

Sign in / Sign up

Export Citation Format

Share Document