Application of multivariate analysis to determine spatial and temporal changes in water quality after new channel construction in the Chilika Lagoon

Kim, Ji Yoon; Bhatta, K. S.; Rastogi, Gurdeep; Muduli, Pradipta R.; Do, Yuno; Kim, Dongkyun; Pattnaik, Ajit K.; Joo, Gea‐Jae

doi:10.1016/j.ecoleng.2016.01.053

Cited by 22 publications

(6 citation statements)

References 33 publications

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“…Variation in water quality of Coastal lagoon is regularly attributed to anthropogenic activities, as well as natural processes such as hydrological features, precipitation, etc. The input of freshwater and domestic sewage effluent into coastal lagoon waters may have several ecological consequences such as reduce in salinity, organic matter and nutrients increase; algal blooms, formation of hypoxia or anoxia in the bottom water and change in the phytoplankton community structure [2] In Juan polo lagoon, uncontrolled urbanization, high population growth, fuel wood gathering and the lack of residual water treatment facilities are among the major factors threatening mangrove and wetland ecosystems. Different ecotourism activities are being carried out in the lagoon by the natives as an economical opportunity to improve their life, but these activities may also put in risk the persons that take these ecological tours [3].…”

Section: Introductionmentioning

confidence: 99%

Multivariable statistical evaluation of water quality in Juan polo coastal lagoon (Colombian Caribbean)

Baldiris-Navarro¹,

Sanchez-Aponte²,

González-Delgado³

et al. 2018

ces

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The aim of this paper was to evaluate the status and trends of water quality in a stressed coastal lagoon in Cartagena, Colombian Caribbean. The water quality data over an eight-year period was analyzed. The study was focused on water parameters as dissolved oxygen (DO), biochemical oxygen demand (BOD5), chemical oxygen demand (COD), salinity, pH, total dissolved solids, total coliforms (TC), ammonium (NH4 +) and total phosphorus (TP). The analysis was conducted in line with Colombian national water standard. Results showed that BOD5, COD and phosphorus are out of the limits for this variables in Colombia. Principal components analysis detected four components that explain 70 % of the variance of data and showed that anthropogenic and temporal factors might be affecting the variation. 1340 Ildefonso Baldiris-Navarro et al.

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Section: Introductionmentioning

confidence: 99%

Multivariable statistical evaluation of water quality in Juan polo coastal lagoon (Colombian Caribbean)

Baldiris-Navarro¹,

Sanchez-Aponte²,

González-Delgado³

et al. 2018

ces

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“…Elevated PO 4 3-(and consequently TP) and NH 3 levels, as well as high turbidity, may be the result of domestic sewage discharge, since PO 4 3is an important component of detergents, and NH 3 occurs through the decomposition of organic compounds that contain nitrogen, such as proteins and urea, thus indicating a strong correlation with BOD, which expresses organic matter pollution (VEGA et al, 1998;WU et al, 2010, SCHAFFELKEM et al, 2012JHA et al, 2015). The second group, composed of salinity and EC, is directly related to the degree of water exchange between the bay and the ocean (KIM et al, 2016). Given that the first factor always explained the greatest data variability and represents the most common elements of the variables studied, it was adopted as the Guanabara Bay Water Quality Index (WQI GB ) (DEVEREL, 1989;FEPAM, 2005;COLETTI et al, 2010).…”

Section: Resultsmentioning

confidence: 99%

Proposal of a Water Quality Index for Guanabara Bay (Rio De Janeiro, Brazil) with a View to Reducing Costs Related to the Environmental Monitoring

Mencarini¹,

Guimaraes²,

Yokoyama³

2018

SBIJ

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In this work, multivariate statistical methods were used to develop a specific Water Quality Index for Guanabara Bay, in order to reduce the costs related to the environmental monitoring of this estuary, without loss of information quality. The study area included fourteen monitoring stations and related data of thirteen selected parameters frequently monitored during 2010 to 2016. Factor analysis was applied to the water quality parameters to identify the relationships between them and to identify the parameters that were most significant. Consequently, the number of monitored parameters was reduced to seven (BOD, electrical conductivity, total phosphorus, total ammoniacal nitrogen, orthophosphate, turbidity and salinity), which were then considered in the Guanabara Bay Water Quality Index (WQIGB). WQIGB is an easy-to-understand index developed for Guanabara Bay that classifies water quality into three ranges (good, fair and poor), as well as provides a significant reduction of costs related to sampling and laboratory analysis.

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“…Once the SIMPROF groups were defined and analyzed, the analysis of main components (PCA) of normalized data of the physicochemical parameters of water [Yoon et al 2016] was carried out to generate perceptual maps. The Scree plot analysis allowed access to the components that explain the greater variability in the data and with it determine the distances of similarity between the groupings by sites.…”

Section: Discussionmentioning

confidence: 99%

Variability of the Water Quality Characterizing High Andean Lagoons for Tourist Use Evaluated Through Multivariate Statistical Methods, Junín, Peru

et al. 2019

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The spatial-temporal variability characterizing the water quality of high Andean lagoons for tourist use was evaluated using multivariate statistical methods during 2017 and 2018. The water samples were collected from 14 sampling sites, with three replicates each. The water quality indicators determined were: pH, temperature, DO, COD, BOD 5 , P, N, Fe, Cu, Cr, Cd, Pb, Zn and chlorophyll-a. The flat cluster analysis (k R cluster) according to Ward's algorithm showed six significantly differentiated groups (α=0.01). In turn, the real similarity profile (SIMPROF) moves markedly away from the obtained low permutation with a large excess of Euclidean similarity with a Pi value of 0.627. The PCA showed that the first two components recommended by the sedimentation analysis (Scree test) indicated 61.52% of the total variation of the observations. According to the Spearman range correlation selection criterion, the variables that best interpret the sample distributions are COD, DTS, P, Cd and Zn with a correlation of 0.893, the DTS being the most important variable with a correlation value of 0.795. The PERMANOVA analysis according to the flat cluster factor indicated that at least one of the groups is different from the others in relation to the levels of physicochemical characteristics studied. Therefore, all the configured groups are statistically different, demonstrating that each lagoon is different in relation to its physicochemical indicators, according to the season in which it is found.

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Application of multivariate analysis to determine spatial and temporal changes in water quality after new channel construction in the Chilika Lagoon

Cited by 22 publications

References 33 publications

Multivariable statistical evaluation of water quality in Juan polo coastal lagoon (Colombian Caribbean)

Multivariable statistical evaluation of water quality in Juan polo coastal lagoon (Colombian Caribbean)

Proposal of a Water Quality Index for Guanabara Bay (Rio De Janeiro, Brazil) with a View to Reducing Costs Related to the Environmental Monitoring

Variability of the Water Quality Characterizing High Andean Lagoons for Tourist Use Evaluated Through Multivariate Statistical Methods, Junín, Peru

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