Phytoplankton in relation to water quality of Tanguar Haor ecosystem, Bangladesh: I. Rauar station

The water quality at Rauar station of Tanguar Haor, Sunamganj was assessed studying phytoplankton and associated environmental variables. The environmental variables were monitored over a period of one year, collecting samples at two months interval between March, 2017 and March, 2018. Air temperature, rainfall, and humidity ranged from 22.6 - 32.1°C, 48 - 76% and 8 - 930 mm, respectively. Air temperature showed a direct relationship with water temperature which varied from 22.4 - 31.0°C during the study period. The water transparency remained relatively constant throughout the year having a mean Secchi depth (Zs) value of 2.48 m. Total dissolved solids (TDS), conductivity, and pH of the water ranged from 51 - 85 mg/l, 60 - 110 μS/cm, and 7.2 - 9.7, respectively. In December, because of a temperature fall, the dissolved oxygen (DO) concentration of the water reached its maximum value of 6.09 mg/l. In the rest of the period, the concentration remained between 2.44 and 4.80 mg/l. The value of alkalinity ranged from 0.43 - 1.35 meq/l. Among the nutrients, soluble reactive phosphorus (SRP), soluble reactive silicate (SRS), and NO3-N ranged from 5.43 - 36.43 μg/l, 4 - 14.58 mg/l, and 0.06 - 0.31 mg/l, respectively. The concentration of NH4+ ranged from 238 - 1230 μg/l. The highest concentrations (905 and 1230 μg/l) occurred between September and December, 2017. This might be attributed to the higher density of migratory birds during that period. The phytoplanktonic biomass expressed as chlorophyll-a (Chl-a) ranged from 1.35 - 8.45 μg/l while its degraded product phaeophytin concentration ranged from 0.08 - 3.5 μg/l. The standing crop of phytoplankton ranged from 397 - 2480 × 103 individuals/l of haor water exhibiting its maximum abundance in September. This parameter showed a highly significant positive correlation with SRP. From the correlation analysis, the degradation of chl-a to phaeophytin was found to be temperature dependent. Considering the different physicochemical and biological water quality data, it could be said that the Tanguar Haor is still free from organic pollution. However, the range of soluble reactive phosphorus data (5.43 - 36.43 μg/l) show that the Haor has been passing a meso-eutrophic state. Dhaka Univ. J. Biol. Sci. 28(2): 131-138, 2019 (July)

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Effects of water quality on phytoplankton biomass in Balu river, Dhaka, Bangladesh

Journal of Biodiversity Conservation and Bioresource Management ◽

10.3329/jbcbm.v6i1.51330 ◽

2021 ◽

Vol 6 (1) ◽

pp. 37-46

Author(s):

MAH Bhuiyan ◽

MR Islam ◽

SA MS Islam ◽

A Kowser ◽

M Mohid ◽

...

Keyword(s):

Water Quality ◽

Water Temperature ◽

Phytoplankton Biomass ◽

Secchi Depth ◽

Soluble Reactive Phosphorus ◽

Co2 Concentration ◽

Quality Parameters ◽

Total Density ◽

Chl A ◽

Reactive Phosphorus

The Balu River is a peripheral river of Dhaka Metropolis and like other rivers its water quality is also highly vulnerable towards pollution. In the present study, effects of water quality parameters on the phytoplankton biomass have been studied for six climatic seasons of the year. The results obtained were compared with two other rivers of Dhaka Metropolis namely, Buriganga and Turag. Balu River showed water quality characters almost comparable with two other rivers. The phytoplankton biomass as chlorophyll a (chl-a) was found to be correlated directly with air and water temperature and alkalinity of the water. However, a weak positive correlation was obtained between chl-a and Secchi depth and free CO2 concentration. Nitrate nitrogen showed weak negative correlation with chl-a and phaeopigment and phytoplankton total density. High fluctuation was observed in the concentration of DO (0.20 - 4.50 mg/l) and free CO2 (0.06 - 2.90 mg/l) throughout the seasons. Soluble reactive phosphorus ranged from 30 - 1248 μg/l and chl-a ranged from 6.77 - 32.60 μg/l. Phytoplankton density ranged 1178 - 7409×103 ind./l over the study year. Water color ranged from light blue to dark black. The depth of visibility as Secchi depth varied from 15.24 - 81.28 cm whilst the water depth of the studied station Paschim Gaon of Balu River ranged from 4.57 - 7.92 m. Air and water temperature ranged from 25.4 - 36.5°C and 23.4 - 32.3°C, respectively. J. Biodivers. Conserv. Bioresour. Manag. 2020, 6(1): 37-46

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

Jurnal Teknologi ◽

10.11113/jt.v77.4054 ◽

2015 ◽

Vol 77 (1) ◽

Cited By ~ 3

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

Journal of Korean Society of Environmental Engineers ◽

10.4491/ksee.2021.43.3.171 ◽

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.

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ESTIMATION OF CHLOROPHYLL-A CONCENTRATION IN SAMPALOC LAKE USING UAS MULTISPECTRAL REMOTE SENSING AND REGRESSION ANALYSIS

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-4-w19-297-2019 ◽

2019 ◽

Vol XLII-4/W19 ◽

pp. 297-303

Author(s):

R. M. G. Maravilla ◽

J. P. Quinalayo ◽

A. C. Blanco ◽

C. G. Candido ◽

E. V. Gubatanga ◽

...

Keyword(s):

Water Quality ◽

Regression Analysis ◽

Chlorophyll A ◽

Linear Regression Analysis ◽

Quality Data ◽

Water Quality Data ◽

Microalgal Biomass ◽

Chlorophyll A Concentration ◽

Chl A

Abstract. Sampaloc Lake is providing livelihood for the residents through aquaculture. An increase in the quantity of fish pens inside the lake threatens its water quality condition. One parameter being monitored is microalgal biomass by measuring Chlorophyll-a concentration. This study aims to generate a chlorophyll-a concentration model for easier monitoring of the lake. In-situ water quality data were collected using chl-a data logger and water quality meter at 357 and 12 locations, respectively. Using Parrot Sequoia+ Multispectral Camera, 1496 of 2148 images were acquired and calibrated, producing 18x18cm resolution Green (G), Red(R), Red Edge (RE) and Near Infrared (NIR) reflectance images. NIR was used to mask out non-water features, and to correct sun glint. The in-situ data and the pixel values extracted were used for Simple Linear Regression Analysis. A model with 5 variables – R/NIR, RE2, NIR2, R/NIR2, and NIR/RE2, was generated, yielding an R2 of 0.586 and RMSE of 0.958 μg/l. A chlorophyll-a concentration map was produced, showing that chl-a is higher where fish pens are located and lowers as it moves away from the pens. Although there are apparent fish pens on certain areas of the lake, it still yields low chlorophyll-a because of little amount of residential area or establishments adjacent to it. Also, not all fish pens have the same concentration of Chlorophyll-a due to inconsistent population per fish pen. The center of the lake has low chlorophyll-a as it is far from human activities. The only outlet, Sabang Creek, also indicates high concentration of Chlorophyll-a.

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Effects of Saline Flushing to a Polluted Estuary to Enhance Water Quality Standards

Water Science & Technology ◽

10.2166/wst.1989.0045 ◽

1989 ◽

Vol 21 (2) ◽

pp. 167-176 ◽

Cited By ~ 1

Author(s):

P. M. Nuttall ◽

B. J. Richardson ◽

P. Condina

Keyword(s):

Water Quality ◽

Aquatic Plant ◽

Organic Pollution ◽

Quality Data ◽

Fish Movement ◽

Water Quality Standards ◽

Cyanobacterial Mats ◽

Phytoplankton Blooms ◽

Water Quality Data ◽

Water Species

Kananook Creek, a polluted estuary in urban Victoria,was monitored for water quality data over a seven year period. Prior to saline flushing, low species diversity dominated by high numbers of organic pollution-tolerant macroinvertebrates, phytoplankton blooms and cyanobacterial mats occurred throughout the estuary in clearly defined zones. Low dissolved oxygen levels restricted fish movement. Sand and silt deposition in the estuary prevented submergent aquatic plant colonisation, primarily as a result of the unstable, shifting nature of the substratum. Subsequent saline flushing at a maximum continuous rate of 150 ML/day saltwater from a coastal waterway improved quality within the water column of the polluted estuary. Although flushing reduced the incidence of fresh-water species, estuarine fauna and flora rapidly colonised much of Kananook Creek. The incidence of phytoplankton blooms, water discolouration and odour was reduced to the benefit of recreation demands placed upon the creek. Polluted and unstable sediments continued to restrict macroinvertebrate establishment and occasional cessation in flushing for pump maintenance caused a rapid deterioration in water quality.

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Machine Learning modeling techniques for forecasting the trophic level in a restored South Mediterranean lagoon using Chlorophyll-α

10.21203/rs.3.rs-331963/v1 ◽

2021 ◽

Author(s):

Nadia Ben Hadid ◽

Catherine Goyet ◽

Hatem Chaar ◽

Naceur Ben Maiz ◽

Veronique Guglielmi ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

Secchi Depth ◽

Quality Data ◽

Predictor Variables ◽

Water Quality Data ◽

Chl A ◽

The North ◽

Artificial Neural Network Ann ◽

Narx Model

Abstract An Artificial Neural Network (ANN), a Machine Learning (ML) modeling approach is proposed to predict the ecological state of the North Lagoon of Tunis, a shallow restored Mediterranean coastal ecosystem. A Nonlinear Auto Regressive with exogenous input (NARX) neural network model was fitted to predict Chlorophyll- a (Chl- a ) concentrations in the North Lagoon of Tunis as an eutrophication indicator. The modeling is based on approximately three decades of monitoring water quality data (from January 1989 to April 2018) to train, validate and test the NARX model. The most relevant predictor variables used in this model were those having a high permutation importance ranking with Random Forest (RF) model, which simplified the structure of the network, resulting in a more accurate and efficient procedure. Those predictor variables are secchi depth, and dissolved oxygen. Various model scenarios with different NARX architectures were tested for Chl- a prediction. To verify the model performances, the trained models were applied to field monitoring data. Results indicated that the developed NARX model can predict Chl- a concentrations in the North Lagoon of Tunis with high accuracy (R= 0.79; MSE= 0.31). In addition, results showed that the NARX model generally performed better than multivariate linear regression (MVLR). This approach could provide a quick assessment of Chl- a variations for lagoon management and eco-restoration.

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