scholarly journals Space time evolution of the trophic state of a subtropical lagoon: Lagoa da Conceição, Florianópolis Island of Santa Catarina, Brazil

RBRH ◽  
2017 ◽  
Vol 22 (0) ◽  
Author(s):  
Victor Eduardo Cury Silva ◽  
Davide Franco ◽  
Alessandra Larissa Fonseca ◽  
Maria Luiza Fontes ◽  
Alejandro Rodolfo Donnangelo
Keyword(s):  
Water Column ◽  
Trophic Level ◽  
Algal Blooms ◽  
Trophic State ◽  
Coastal Lagoons ◽  
Principal Component ◽  
Quality Data ◽  
Santa Catarina ◽  
Water Quality Data ◽  
Subtropical Lagoon

ABSTRACT High levels of eutrophication in coastal lagoons due to human activity have been documented worldwide. Among the main impacts observed are anoxia, hypoxia, toxic algal blooms, fish kills, loss of biodiversity and loss of bathing. This study aimed to evaluate the evolution of the trophic state of Lagoa da Conceição, a subtropical lagoon located in an urbanized watershed on the island of Santa Catarina - Brazil. Spatio temporal patterns of stratification and eutrophication were investigated to understand the main biochemical changes over time. The water quality data were obtained from field campaigns supplemented with literature of the last 15 years. The vertical structure of the water column and the trophic state were evaluated by the stratification index and the TRIX index, respectively. Analyses of variance were performed in order to identify possible temporal variations in vertical stratification and trophic level. Eutrophication effects on biogeochemical cycles were verified through a multi-dimensional cluster analysis (MDS) and correlations between variables related to physical, chemical and biological processes were verified by principal component analysis (PCA). The results showed that the water column is homogeneous in all regions except in the central region of the lagoon, and the highest ammonia concentrations and lowest dissolved oxygen concentrations with periods of anoxia are observed in bottom waters. The study looked at the high trophic level of the lagoon and its inability to process the biogeochemical changes imposed by urban development.

scholarly journals Control limit detection for source apportionment in Perlis River Basin, Malaysia

2017 ◽  
Vol 13 (3) ◽  
Author(s):  
Mohd Saiful Samsudin ◽  
Saiful Iskandar Khalit ◽  
Azman Azid ◽  
Hafizan Juahir ◽  
Ahmad Shakir Mohd Saudi ◽  
...  
Keyword(s):  
River Basin ◽  
Control Process ◽  
Principal Component ◽  
River Pollution ◽  
Quality Data ◽  
Water Quality Data ◽  
Data Set ◽  
Hierarchical Agglomerative Cluster ◽  
Limit Detection ◽  
Hierarchical Agglomerative Cluster Analysis

This study presents the application of selected environmetric in the Perlis River Basin. The results show PCA extracted nine principal components (PCs) with eigenvalues greater than one, which equates to about 77.15% of the total variance in the water-quality data set. The absolute principal component scores (APCS)-MLR model discovered BOD and COD as the main parameters, which indicates the measure of the agricultural pollution in the Perlis River Basin, the hierarchical agglomerative cluster analysis (HACA) shows 11 monitoring stations assembled into two clusters in accordance with similarities in the concentration of BOD and COD, which are grouped in P4. The X ̅ control chart shows that the mean concentration of BOD and COD in P4 is in the control process. The capability ratio (Cp) was applied to measure the risk of the concentration in terms of the river pollution in a subsequent period of time using the limit NWQS.

Nutrients and Algal Growth in an Impounded River, Consequences for its Oxygen Balance and Nutrient Control Strategy

1982 ◽  
Vol 14 (4-5) ◽  
pp. 185-197 ◽  
Author(s):  
K R Imhoff ◽  
D R Albrecht
Keyword(s):  
Algal Blooms ◽  
Algal Growth ◽  
Oxygen Demand ◽  
Quality Data ◽  
Oxygen Balance ◽  
Phosphate Content ◽  
Water Quality Data ◽  
Artificial Aeration ◽  
The Past ◽  
Oxygen Shortage

A series of impoundments occur on the final 46 km stretch of the Ruhr river where phosphorus, nitrogen, and organic carbon are present abundantly, causing several heavy algal blooms during the year. This is detrimental for the treatment capacity of the water works. Also, an oxygen shortage is always recorded in the river after algal decay at low flows, thus requiring artificial aeration. By balancing all oxygen supply and consumption it is shown that about 2/3 of the oxygen demand is due to algal decomposition. When evaluating the water quality data of the past 30 years and by conducting special laboratory tests, results show that phosphates initiate algal growth. Therefore, a phosphate model has been developed for the river which predicts the phosphate content for 1988 and 1998. It is expected that by reduction of this phosphate content maximum algal growth can be cut by about 50 %.

scholarly journals Study of groundwater contamination and drinking suitability in basaltic terrain of Maharashtra, India through PIG and multivariate statistical techniques

2020 ◽  
Vol 69 (4) ◽  
pp. 398-414 ◽  
Author(s):  
Vasant Wagh ◽  
Shrikant Mukate ◽  
Aniket Muley ◽  
Ajaykumar Kadam ◽  
Dipak Panaskar ◽  
...  
Keyword(s):  
Major Ions ◽  
Principal Component ◽  
World Health ◽  
Quality Data ◽  
Statistical Techniques ◽  
Multivariate Statistical Techniques ◽  
Multivariate Statistical ◽  
Water Quality Data ◽  
Post Monsoon ◽  
Groundwater Samples

Abstract The integration of pollution index of groundwater (PIG), multivariate statistical techniques including correlation matrix (CM), principal component analysis (PCA), cluster analysis (CA) and various ionic plots was applied to elucidate the influence of natural and anthropogenic inputs on groundwater chemistry and quality of the Kadava river basin. A total of 80 groundwater samples were collected and analysed for major ions during pre- and post-monsoon seasons of 2012. Analytical results inferred that Ca, Mg, Cl, SO4 and NO3 surpass the desirable limit (DL) and permissible limit (PL) of Bureau of Indian Standards (BIS) and the World Health Organization (WHO) in both the seasons. The elevated content of total dissolved solids (TDS), Cl, SO4, Mg, Na and NO3 is influenced by precipitation and agricultural dominance. PIG results inferred that 52.5 and 35%, 30 and 37.5%, 12.5 and 20%, 2.5 and 5% groundwater samples fall in insignificant, low, moderate and high pollution category (PC) in pre- and post-monsoon seasons, respectively. PC 1 confirms salinity controlled process due to high inputs of TDS, Ca, Mg, Na, Cl and SO4. Also, PC 2 suggests alkalinity influence by pH, CO3, HCO3 and F content. PIG and statistical techniques help to interpret the water quality data in an easier way.

Hydrology and Water Quality Survey Near the Gezhouba Dam in the Winter

2013 ◽  
Vol 726-731 ◽  
pp. 3256-3261
Author(s):  
Jia Fei Zhou ◽  
Cong Feng Wang ◽  
De Fu Liu ◽  
Jing Wen Xiang ◽  
Ping Zhao ◽  
...  
Keyword(s):  
Water Quality ◽  
Dissolved Oxygen ◽  
Water Temperature ◽  
Flow Velocity ◽  
Water Column ◽  
Quality Data ◽  
Chinese Sturgeon ◽  
Water Quality Data ◽  
Gezhouba Dam ◽  
Survival Condition

Filed hydrology and water quality data were collected near the Gezhouba Dam early December of 2012 to analyze the response of Chinese Sturgeon survival condition to water temperature, dissolved oxygen (DO), pH, transparency (SD) and bottom flow-velocity. The results showed that water temperature lag is unconspicuous. The water temperature of Gezhouba Dam Sanjiang (GDS) was lower than that of Gezhouba Dam River (GDR), and it hindered propagation of sturgeon eggs. DO decreased fast in the vertical water column of GDS, pH ranged from 7.5 to 7.71. The hydrology and water quality were suitable for the life condition of sturgeon eggs and fry, except index of bottom flow-velocity.

scholarly journals A Multivariate and Spatiotemporal Analysis of Water Quality in Code River, Indonesia

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Mochamad A. Pratama ◽  
Yan D. Immanuel ◽  
Dwinanti R. Marthanty
Keyword(s):  
Water Quality ◽  
Land Cover ◽  
Agricultural Land ◽  
Water Quality Management ◽  
Temporal Trends ◽  
Oxygen Demand ◽  
Principal Component ◽  
Water Quality Monitoring ◽  
Quality Data ◽  
Water Quality Data

The efficacy of a water quality management strategy highly depends on the analysis of water quality data, which must be intensively analyzed from both spatial and temporal perspectives. This study aims to analyze spatial and temporal trends in water quality in Code River in Indonesia and correlate these with land use and land cover changes over a particular period. Water quality data consisting of 15 parameters and Landsat image data taken from 2011 to 2017 were collected and analyzed. We found that the concentrations of total dissolved solid, nitrite, nitrate, and zinc had increasing trends from upstream to downstream over time, whereas concentrations of parameter biological oxygen demand, cuprum, and fecal coliform consistently undermined water quality standards. This study also found that the proportion of natural vegetation land cover had a positive correlation with the quality of Code River’s water, whereas agricultural land and built-up areas were the most sensitive to water pollution in the river. Moreover, the principal component analysis of water quality data suggested that organic matter, metals, and domestic wastewater were the most important factors for explaining the total variability of water quality in Code River. This study demonstrates the application of a GIS-based multivariate analysis to the interpretation of water quality monitoring data, which could aid watershed stakeholders in developing data-driven intervention strategies for improving the water quality in rivers and streams.

scholarly journals Comprehensive Assessment of Water Quality and Pollution Source Apportionment in Wuliangsuhai Lake, Inner Mongolia, China

2020 ◽  
Vol 17 (14) ◽  
pp. 5054 ◽  
Author(s):  
Rui Shi ◽  
Jixin Zhao ◽  
Wei Shi ◽  
Shuai Song ◽  
Chenchen Wang
Keyword(s):  
Water Quality ◽  
Inner Mongolia ◽  
Principal Component ◽  
Middle Part ◽  
Quality Data ◽  
Quality Factors ◽  
Water Quality Data ◽  
Irrigation Area ◽  
Irrigation Period ◽  
Hetao Irrigation Area

Water quality is a key indicator of human health. Wuliangsuhai Lake plays an important role in maintaining the ecological balance of the region, protecting the local species diversity and maintaining agricultural development. However, it is also facing a greater risk of water quality deterioration. The 24 water quality factors that this study focused on were analyzed in water samples collected during the irrigation period and non-irrigation period from 19 different sites in Wuliangsuhai Lake, Inner Mongolia, China. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) were conducted to evaluate complex water quality data and to explore the sources of pollution. The results showed that, during the irrigation period, sites in the middle part of the lake (clusters 1 and 3) had higher pollution levels due to receiving most of the agricultural and some industrial wastewater from the Hetao irrigation area. During the non-irrigation period, the distribution of the comprehensive pollution index was the opposite of that seen during the irrigation period, and the degree of pollutant index was reduced significantly. Thus, run-off from the Hetao irrigation area is likely to be the main source of pollution.

scholarly journals Evaluation of water pollution and source identification in Merbok River Kedah, Northwest Malaysia

2020 ◽  
Vol 16 (4) ◽  
pp. 458-463
Author(s):  
Ateshan Msahir Haidr ◽  
Misnan Rosmilah ◽  
Sinang Som Cit ◽  
Koki Baba Isa
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Anthropogenic Activities ◽  
Human Health Risk ◽  
Principal Component ◽  
Component Analysis ◽  
Quality Data ◽  
Toxic Heavy Metals ◽  
Water Quality Data ◽  
Fish And Shellfish

This study investigates the temporal water quality variations and pollution sources identification in Merbok River using principal component analysis. The variables analyzed include As, Cd, Pb, Fe, Cr, Mn, Zn, Ni, Ca, Mg, Na, K, NH4, F, Cl, Br, NO2, NO3, SO4, PO4, pH, BOD, DO, COD, turbidity, and salinity. These variables were analyzed using inductively coupled plasma mass spectrometry, ion chromatography, and YSI multiprobe. Principal component analysis (PCA) was utilized to evaluate the variations of the most significant water quality parameters and identify the probable source of the pollutants. From the results of PCA, 86% of the total variations were observed in the water quality data with strong dominance of toxic heavy metals (As, Pb, and Cr), parameters associated with industrial discharge, domestic inputs, overland runoff (NH4, pH, BOD, DO, COD), agrochemicals (NO2, NO3, SO4, PO4), and weathering of basement rocks (Ca, Mg, Cl, F, K, and Na). Most of these parameters were present in concentrations exceeded the reference standards limits used in this study, indicating pollution of the river water. Together with the presence of microbial contamination, the results suggest potential human health risk due to water uses, fish and shellfish consumption. Moreover, the results revealed that anthropogenic activities and weathering were the main sources of pollutants in Merbok River. 

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 Multivariate Analysis of Water Quality Measurements on the Danube River

Water ◽  
2021 ◽  
Vol 13 (24) ◽  
pp. 3634
Author(s):  
Zoltan Horvat ◽  
Mirjana Horvat ◽  
Kristian Pastor ◽  
Vojislava Bursić ◽  
Nikola Puvača
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Multivariate Analysis ◽  
Cross Sections ◽  
Principal Component ◽  
Component Analysis ◽  
Danube River ◽  
Quality Data ◽  
Water Quality Data ◽  
The Danube River

This study investigates the potential of using principal component analysis and other multivariate analysis techniques to evaluate water quality data gathered from natural watercourses. With this goal in mind, a comprehensive water quality data set was used for the analysis, gathered on a reach of the Danube River in 2011. The considered measurements included physical, chemical, and biological parameters. The data were collected within seven data ranges (cross-sections) of the Danube River. Each cross-section had five verticals, each of which had five sampling points distributed over the water column. The gathered water quality data was then subjected to several multivariate analysis techniques. However, the most attention was attributed to the principal component analysis since it can provide an insight into possible grouping tendencies within verticals, cross-sections, or the entire considered reach. It has been concluded that there is no stratification in any of the analyzed water columns. However, there was an unambiguous clustering of sampling points with respect to their cross-sections. Even though one can attribute these phenomena to the unsteady flow in rivers, additional considerations suggest that the position of a cross-section can have a significant impact on the measured water quality parameters. Furthermore, the presented results indicate that these measurements, combined with several multivariate analysis methods, especially the principal component analysis, may be a promising approach for investigating the water quality tendencies of alluvial rivers.

SPATIAL CHARACTERIZATION AND IDENTIFICATION SOURCES OF POLLUTION USING MULTIVARIATE ANALYSIS AT TERENGGANU RIVER BASIN, MALAYSIA

2015 ◽  
Vol 77 (1) ◽  
Author(s):  
Ahmad Firdaus Kamaruddin ◽  
Mohd Ekhwan Toriman ◽  
Hafizan Juahir ◽  
Sharifuddin Md Zain ◽  
Mohd Nordin Abdul Rahman ◽  
...  
Keyword(s):  
Water Quality ◽  
Principal Component ◽  
Organic Pollution ◽  
Agricultural Runoff ◽  
Pollution Sources ◽  
Anthropogenic Sources ◽  
Quality Data ◽  
Water Quality Data ◽  
Hierarchical Agglomerative Cluster ◽  
Sources Of Pollution

The spatial water quality data (281x22) obtained from 12 sampling stations located along the Terengganu River and its main tributaries were evaluated with environmetric methods. Principal component analysis was used to investigate the origin of each variable due to land use and human activities based on the three clustered regions obtained from the hierarchical agglomerative cluster analysis. Six principal components (PCs) were obtained, where six varimax factor (VF) of values more than 0.70 that considered strong loading are discussed. The possible pollution sources identified are of anthropogenic sources, mainly municipal waste, surface runoff, agricultural runoff, organic pollution and urban storm runoff. As a conclusion, the application of environmetric methods could reveal important information on the spatial variability of a large and complex river water quality data in order to control pollution sources.

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