scholarly journals Application of multivariate statistical techniques for investigating climate change and anthropogenic effects on surface water quality assessment: case study of Zohreh river, Hendijan, Iran

2021 ◽  
Vol 11 (6) ◽  
Author(s):  
Jalal Valiallahi ◽  
Saideh Khaffaf Roudy
Keyword(s):  
Water Quality ◽  
Cluster Analysis ◽  
Factor Analysis ◽  
Monitoring Program ◽  
T Test ◽  
Total Variance ◽  
Quality Data ◽  
Water Quality Data ◽  
Significant Difference ◽  
The Mean

AbstractIn the present study, evaluation of spatial variations and interpretation of Zohrehh River water quality data were made by using multivariate analytical techniques including factor analysis and cluster analysis also the Arc GIS® software was used. The research method was formulated to achieve objectives herein, including field observation, numerical modeling, and laboratory analyses. The results showed that dataset consisted of 11,250 observations of seven-year monitoring program (measurement of 15 variables at 3 main stations from April 2010 to March 2017). Factor analysis with principal component analysis extraction of the dataset yielded seven varactors contributing to 82% of total variance and evaluated the incidence of each varactor on the total variance. The results of cluster analysis became complete with t-test and made water quality comparison between two clusters possible. Results of factor analysis were employed to facilitate t-test analysis. The t-test revealed the significant difference in a confidence interval of 95% between the mean of calculated varactors 1, 2, 6 and 7 between two clusters, but there was no significant difference in the mean of other varactors 3, 4 and 5 between two groups. The result shows the effect of agricultural fertilizers on stations located at downstream of the ASK dam.

scholarly journals Assessment of Tigris River Water Quality Using Multivariate Statistical Techniques

2019 ◽  
Vol 26 (4) ◽  
pp. 26-31
Author(s):  
Muntasir Shareef
Keyword(s):  
Water Quality ◽  
Factor Analysis ◽  
Total Variance ◽  
Quality Data ◽  
Statistical Techniques ◽  
Complex Nature ◽  
Multivariate Statistical Techniques ◽  
Multivariate Statistical ◽  
Water Quality Data ◽  
Tigris River

The present study uses the multivariate statistical techniques by applying the Factor Analysis (Principle component method) to explain the observed water quality data of Tigris river within Baghdad city. The water quality was analyzed at eleven different sites, along the river, over a period of one year (2017) using 20 water quality parameters. Five factors were identified by factor analysis which was responsible from the 72.291% of the total variance of the water quality in the Tigris river. The first factor called the pollution factor explained 34.387% of the total variance and the second factor called the surface runoff and erosion factor explained 11.875% of the total variance. While, the third, fourth, and fifth factors explained 10.213%, 8.861% and 6.956% of the total variance and called pH, Silica and nutrient factors, respectively. Multivariate statistical techniques can be effective methods to aid water resources managers understand complex nature of water quality issues and determine the priorities to sustain water quality.

scholarly journals Visualizing Water Quality Sampling-Events in Florida

2015 ◽  
Vol II-4/W2 ◽  
pp. 73-79 ◽  
Author(s):  
M. D. Bolt
Keyword(s):  
Water Quality ◽  
Dissolved Oxygen ◽  
Data Visualization ◽  
Coastal Water ◽  
Environmental Quality ◽  
Additional Data ◽  
Monitoring Program ◽  
Quality Data ◽  
Water Quality Data ◽  
Environmental Narratives

Water quality sampling in Florida is acknowledged to be spatially and temporally variable. The rotational monitoring program that was created to capture data within the state’s thousands of miles of coastline and streams, and millions of acres of lakes, reservoirs, and ponds may be partly responsible for inducing the variability as an artifact. Florida’s new dissolved-oxygen-standard methodology will require more data to calculate a percent saturation. This additional data requirement’s impact can be seen when the new methodology is applied retrospectively to the historical collection. To understand how, where, and when the methodological change could alter the environmental quality narrative of state waters requires addressing induced bias from prior sampling events and behaviors. Here stream and coastal water quality data is explored through several modalities to maximize understanding and communication of the spatiotemporal relationships. Previous methodology and expected-retrospective calculations outside the regulatory framework are found to be significantly different, but dependent on the spatiotemporal perspective. Data visualization is leveraged to demonstrate these differences, their potential impacts on environmental narratives, and to direct further review and analysis.

scholarly journals Modelling lake trophic state: A random forest approach

2015 ◽  
Author(s):  
Jeffrey W Hollister ◽  
W. Bryan Milstead ◽  
Betty J. Kreakie
Keyword(s):  
Water Quality ◽  
Random Forests ◽  
Trophic State ◽  
Mean Squared Error ◽  
Quality Data ◽  
Full Model ◽  
Water Quality Data ◽  
Squared Error ◽  
The Mean

Productivity of lentic ecosystems is well studied and it is widely accepted that as nutrient inputs increase, productivity increases and lakes transition from lower trophic state (e.g. oligotrophic) to higher trophic states (e.g. eutrophic). These broad trophic state classifications are good predictors of ecosystem condition, services, and disservices (e.g. recreation, aesthetics, and harmful algal blooms). While the relationship between nutrients and trophic state provides reliable predictions, it requires in situ water quality data in order to parameterize the model. This limits the application of these models to lakes with existing and, more importantly, available water quality data. To address this, we take advantage of the availability of a large national lakes water quality database (i.e. the National Lakes Assessment), land use/land cover data, lake morphometry data, other universally available data, and apply data mining approaches to predict trophic state. Using this data and random forests, we first model chlorophyll a, then classify the resultant predictions into trophic states. The full model estimates chlorophyll a with both in situ and universally available data. The mean squared error and adjusted R2 of this model was 0.09 and 0.8, respectively. The second model uses universally available GIS data only. The mean squared error was 0.22 and the adjusted R2 was 0.48. The accuracy of the trophic state classifications derived from the chlorophyll a predictions were 69% for the full model and 49% for the “GIS only” model. Random forests extend the usefulness of the class predictions by providing prediction probabilities for each lake. This allows us to make trophic state predictions and also indicate the level of uncertainity around those predictions. For the full model, these predicted class probabilites ranged from 0.42 to 1. For the GIS only model, they ranged from 0.33 to 0.96. It is our conclusion that in situ data are required for better predictions, yet GIS and universally available data provide trophic state predictions, with estimated uncertainty, that still have the potential for a broad array of applications. The source code and data for this manuscript are available from https://github.com/USEPA/LakeTrophicModelling.

scholarly journals Modelling lake trophic state: A random forest approach

2015 ◽  
Author(s):  
Jeffrey W Hollister ◽  
W. Bryan Milstead ◽  
Betty J. Kreakie
Keyword(s):  
Water Quality ◽  
Random Forests ◽  
Trophic State ◽  
Mean Squared Error ◽  
Quality Data ◽  
Full Model ◽  
Water Quality Data ◽  
Squared Error ◽  
The Mean

Productivity of lentic ecosystems is well studied and it is widely accepted that as nutrient inputs increase, productivity increases and lakes transition from lower trophic state (e.g. oligotrophic) to higher trophic states (e.g. eutrophic). These broad trophic state classifications are good predictors of ecosystem condition, services, and disservices (e.g. recreation, aesthetics, and harmful algal blooms). While the relationship between nutrients and trophic state provides reliable predictions, it requires in situ water quality data in order to parameterize the model. This limits the application of these models to lakes with existing and, more importantly, available water quality data. To address this, we take advantage of the availability of a large national lakes water quality database (i.e. the National Lakes Assessment), land use/land cover data, lake morphometry data, other universally available data, and apply data mining approaches to predict trophic state. Using this data and random forests, we first model chlorophyll a, then classify the resultant predictions into trophic states. The full model estimates chlorophyll a with both in situ and universally available data. The mean squared error and adjusted R2 of this model was 0.09 and 0.8, respectively. The second model uses universally available GIS data only. The mean squared error was 0.22 and the adjusted R2 was 0.48. The accuracy of the trophic state classifications derived from the chlorophyll a predictions were 69% for the full model and 49% for the “GIS only” model. Random forests extend the usefulness of the class predictions by providing prediction probabilities for each lake. This allows us to make trophic state predictions and also indicate the level of uncertainity around those predictions. For the full model, these predicted class probabilites ranged from 0.42 to 1. For the GIS only model, they ranged from 0.33 to 0.96. It is our conclusion that in situ data are required for better predictions, yet GIS and universally available data provide trophic state predictions, with estimated uncertainty, that still have the potential for a broad array of applications. The source code and data for this manuscript are available from https://github.com/USEPA/LakeTrophicModelling.

scholarly journals Pemberian jenis pakan yang berbeda terhadap pertumbuhan dan kelangsungan hidup benih ikan kakap putih (Lates calcalifer, Bloch)

2017 ◽  
Vol 4 (2) ◽  
pp. 65
Author(s):  
Indra Sahputra ◽  
Munawwar Khalil ◽  
Zulfikar Zulfikar
Keyword(s):  
Water Quality ◽  
Survival Rate ◽  
Suitable Condition ◽  
Sea Bass ◽  
Quality Parameters ◽  
Quality Data ◽  
Feed Consumption ◽  
Water Quality Data ◽  
Randomized Design ◽  
Significant Difference

Penelitian ini dilaksanakan padatanggal 1 Juni – 1 Juli 2014 di Tambak Daerah Cot Kafiraton Kecamatan Seunuddon, Kabupaten Aceh Utara. Metode yang digunakan dalam penelitian ini adalah metode ekperimental dengan menggunakan rancangan acak lengkap (RAL) Non Faktorial dengan lima perlakukan dan tiga kali ulangan yaitu perlakuan A: pemberian pakan jenis udang dogol perlakuan B: pemberian pakan jenis benih ikan nila; perlakuan C: pemberian pakan jenis keong mas ; perlakuan D: pemberian pakan pellet komersial. Parameter uji dalam penelitian ini adalah tingkat kelangsungan hidup, pertumbuhan, kecepatan konsumsi pakan dan kualitas air. Data hasil penelitian dianalisis secara deskriptif serta diuji dengan beda nyata terkecil (BNT). Hasil penelitian menunjukkan bahwa pemberian pakan alami yang berbeda memberi pengaruh yang sangat berbeda nyata terhadap pertumbuhan dan konsumsi pakan pada ikan kakap putih dimana Fhitung >Ftable yaitu pada perlakuan A. Akan tetapi tidak memberi pengaruh yang sangat berbeda nyata terhadap kelangsungan hidup ikan kakap putih. Nilai kualitas air selama penelitian yaitu baik dimana berada pada kisaran yang layak untuk kehidupan ikan kakap putih dengan pH 7,9-8,5, suhu berkisar 25-290C dan salinitas 23-26 ppt.The research was conducted on June 1 to July 1 2014 in Pond at Cot Kafiraton Seunuddon district, North Aceh. The experiment treatments was used on this study using a completely randomized design (CRD) non factorial with five treatments and three replicated which were A: feed types of dogol shrimp, B: feed  type of tilapia seed, treatment C: feed type of snails, treatment D: feed type commercial pellets. Parameters of this study was the survival rate, growth, feed consumption rate and water quality. Data were analyzed descriptively and tested by the least significant difference (LSD). The results was showed that different types of feed had very effect significantly different  on the growth and feed intake of sea bass (Fcal>Ftab). However, different fedd types did not give significantly different influence on the survival rate of sea bass. Water quality parameters were in suitable condition for sea bass habitats. The value of pH was 7,9-8,5, temperature 25-29 0C and salinity  23-26 ppt.

scholarly journals Water Quality Assessment of 14 Reservoirs in Geum River Basin Using Multivariate Statistical Analysis

2021 ◽  
Vol 43 (3) ◽  
pp. 171-186
Author(s):  
Jin Ho Kim ◽  
Jin Chul Joo ◽  
Chae Min Ahn ◽  
Dae Ho Hwang
Keyword(s):  
Water Quality ◽  
Factor Analysis ◽  
Correlation Analysis ◽  
Principal Components ◽  
Organic Pollution ◽  
Quality Characteristics ◽  
Quality Data ◽  
Water Quality Data ◽  
River Watershed ◽  
Internal Production

Objectives : 14 reservoirs in the Geum river watershed were clustered and classified using the results of factor analysis based on water quality characteristics. Also, correlation analysis between pollutants (land system, living system, livestock system) and water quality characteristics was performed to elucidate the effect of pollutants on water quality.Methods : Cluster analysis (CA), principal component analysis (PCA), and factor analysis (FA) using water quality data of 14 reservoirs in the Geum river watershed during the last 5 years (2014-2018) were performed to derive the principal components. Then, correlation analysis between principal components and pollutants was performed to verify the feasibility of clustering.Results and Discussion : From the factor analysis (FA) using water quality data of 14 reservoirs in the Geum river watershed, three to six principal components (PCs) were extracted and extracted PCs explained approximately 74% of overall variations in water quality. As a result of clustering reservoirs based on the extracted PCs, the reservoirs clustered by nitrogen and seasonal PCs were Ganwol, Geumgang, and Sapgyo, the reservoirs clustered by organic pollution and internal production PCs were Tapjung, Dae, Seokmun, and Yongdam, the reservoirs clustered by organic pollution, internal production, and phosphorus are Bunam, Yedang, and Cheongcheon, and finally the remaining Boryeong, Daecheong, Chopyeong, and Songak were clustered as other factors. From the correlation analysis between principal components and pollutants, significant correlation between the land, living, and livestock pollutants and water quality characteristics was found in Ganwol, Topjeong, Daeho, Bunam, and Daecheong. These reservoirs are considered to require continuous and careful management of specific (land, living, livestock) pollutants. In terms of water quality and pollutant management, the Ganwol, Sapgyo, and Seokmunho are considered to implement intensive measures to improve water quality and to reduce the input of various pollutants.Conclusions : Although the water quality of the reservoir is a result of complex interactions such as influent water factors, morphological and hydrological factors, internal production factors, and various pollutants, optimized watershed and water quality management measures can be implemented through multivariate statistical analysis.

scholarly journals Spatiotemporal Water Quality Assessment of Dumate Al-Jandal Lake (KSA) Using Environmetric Techniques

2018 ◽  
Vol 5 ◽  
pp. 412-437
Author(s):  
Mohammed Sharif Al-Sheraideh
Keyword(s):  
Water Quality ◽  
Descriptive Analysis ◽  
Lake Management ◽  
T Test ◽  
The Other ◽  
Multiple Comparison ◽  
Total Alkalinity ◽  
Total Hardness ◽  
Significant Difference ◽  
The Mean

Environmentally, the objective of the study conducted in evaluating the spatiotemporal water quality asessment using some statistical techniques. Physicochemical characteristics determination applied on Dumate al-Jandal Lake, whether the analysis of water quality is good for agricultural irrigation or other ecosystem services.  Sampling and measurements were taken weekly at five sites started from February 2009 to January 2011. Descriptive analysis as well as the 95% confidence intervals, Wilks’ Lambda Statistics, MANOVA and ANOVA showed no presence of significant difference at the level of p < 0.05 among seasons and between sites, except the mean effect only for some parameter like Iron on the physiochemical parameters whereas a significant difference among the mean of physiochemical characteristics of water data to sites for the parameters like pH and nitrate, while there are no differences among the mean of physiochemical characteristics of water data of sites for the other parameters. Multiple comparison t-test shows the differences between means Sites of (1, 3) and (2, 5) as well as between (3) and (5) for the parameter pH.  Results showed a significant difference at (p < 0.05) between the means of Site (1) and all Sites (3, 4 and 5) as well as between Site (2) and (3) for nitrate,  but a significant difference at (p < 0.05)  among the mean associated with seasons for temperature, electrical conductivity, specific density, sulphate, nitrite, ammonia, chloride, total hardness, total alkalinity, manganese, magnesium and calcium hardness, while there are no differences among the mean of seasons for the other parameters. Whereas results of multiple comparison t-test showed that a differences between the means of season (i) and season (j), for each parameter. The study concluded that some of physicochemical parameters were reflected the presence of pollutants and absence of good ecosystem activities. To avoid seasonal pollution, water lake management are recommended.

scholarly journals A Statistical Examination of Water Quality in Two Iowa Lakes

2002 ◽  
Vol 1 (2) ◽  
Author(s):  
Erin Carlson ◽  
Mark Ecker
Keyword(s):  
Water Quality ◽  
Spatial Correlation ◽  
Quality Data ◽  
Water Quality Data ◽  
3 Dimensional ◽  
Significant Difference ◽  
Discriminant Analyses ◽  
The University ◽  
Phosphorus Levels ◽  
Silver Lake

Water quality has become and important issue in the state of Iowa as well as across the entire United States. Two Iowa Lakes, Silver Lake and Casey Lake were chosen for study by a team of biologists, chemists, earth scientists and statisticians from the University of Northern Iowa. Our goals are to statistically compare the water quality in the two lakes in each year and examine whether or not each lake has changed, in terms of water quality variables, from 1999 to 2000. In addition, we explore which variables most affect phosphorus levels in each lake in 2000. Lastly, we explore the spatial distribution of phosphorus in the sediment of each lake. Discriminant Analyses and ANCOVA show significant difference between the two lakes in both 1999 and 2000 as well as a change in Silver Lake's water quality data from 1999 to 2000. Regression Analyses show that, in Silver Lake, phosphorus levels increased during the summer of 2000 while they decreased with increasing levels of surface dissolved oxygen and decreased as the water became less clear. The analyses also show that phosphorus levels in Lake Casey decreased as the water became less clear. A significant relationship between phosphorus in the sediment and depth exists in Lake Casey. While a significant 2-dimensional spatial correlation cannot be shown in Silver Lake, spatial analyses do show the existence of a significant 3-dimensional spatial correlation in Lake Casey.

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