scholarly journals Temporal and Spatial Study of Water Quality and Trophic Evaluation of a Large Tropical Reservoir

Environments ◽  
2019 ◽  
Vol 6 (6) ◽  
pp. 61 ◽  
Author(s):  
Alberto Quevedo-Castro ◽  
Erick R. Bandala ◽  
Jesús G. Rangel-Peraza ◽  
Leonel E. Amábilis-Sosa ◽  
Antonio Sanhouse-García ◽  
...  
Keyword(s):  
Water Quality ◽  
Trophic State ◽  
Probabilistic Neural Network ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Spatial Density ◽  
Trophic State Index ◽  
Tropical Reservoir ◽  
Mean Values ◽  
Limiting Nutrient

A water quality study was carried out at the Adolfo López Mateos (ALM) reservoir, one of the largest tropical reservoirs in Mexico, located within an intensive agricultural region. In this study, the seasonal and spatial variations of nine water quality parameters were evaluated at four different sites along the reservoir semiannually over a period of seven years (2012–2018), considering the spring (dry) and fall (rainy) seasons. An analysis of variance was performed to compare the mean values of the water quality parameters for the different sampling sites. Then, a multiparametric classification analysis was carried out to estimate the spatial density of the sampling points by using a probabilistic neural network (PNN) classifier. The observations (seasonal and spatial) of the water quality parameters at the ALM reservoir revealed no significant influence. The trophic status was evaluated using the Carlson Modified Trophic State Index, finding the trophic state of the reservoir at the mesotrophic level, with nitrogen being the limiting nutrient. The PNN revealed neural interactions between total suspended solids (TSS) and the other four parameters, indicating that the concentration ranges of five parameters are equally distributed and classified.

Modeling the fate, transport and risk of Bisphenol A and N, N-diethyltoluamide in a tropical reservoir

2021 ◽  
Author(s):  
Xuneng Tong ◽  
Jingjie Zhang ◽  
Luhua You ◽  
Karina Yew-Hoong Gin
Keyword(s):  
Water Quality ◽  
Bisphenol A ◽  
Emerging Contaminants ◽  
Fate And Transport ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Trophic State Index ◽  
Tropical Reservoir ◽  
Simulation Performance ◽  
Skill Scores

<p>The fate and transport of emerging contaminants in aquatic environments is a complex process, which is not only determined by their own properties but can also be influenced by the surrounding environment. In this study, a comprehensive modelling framework coupling a 3D hydrodynamic--emerging contaminants module was developed to describe the fate and transport of two representative emerging contaminants, namely Bisphenol A (BPA) and N, N-diethyltoluamide (DEET) in a tropical reservoir. First, the model was calibrated and validated with BPA and DEET obtained from a historical dataset (2013-2014) in bulk water, suspended solids, pore water and sediments phase. Results revealed that the simulation performance gave “excellent simulation” results with skill scores all larger than 0.90. Subsequently, the ecological risk assessment for the reservoir was conducted using the trophic state index (TSI) and coupled species sensitivity distribution (SSD)-Risk Quotient (RQ) method. The RQ values of the study area ranged from 0.003-0.068 (BPA) and 0.001-0.014 (DEET), respectively, which suggests that the levels of studied compounds BPA and DEET may pose low risk to the aquatic ecosystem. Finally, the indirect influence of general water quality parameters such as nutrients (phosphorous) on the multi-compartment distributions of emerging contaminants was explored. Our approach lays down a comprehensive framework to better understand the dynamics of fate and transport and their potential ecological risks of emerging contaminants as well as the indirect impact of other water quality parameters on their distributions in different phases in aquatic ecosystems.</p>

scholarly journals Multivariate Statistical Analysis of Water Quality and Trophic State in an Artificial Dam Reservoir

Water ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 186
Author(s):  
Md Mamun ◽  
Ji Yoon Kim ◽  
Kwang-Guk An
Keyword(s):  
Water Quality ◽  
Trophic State ◽  
Suspended Solids ◽  
Oxygen Demand ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Trophic State Index ◽  
Multivariate Statistical ◽  
Chl A ◽  
State Index

Paldang Reservoir, located in the Han River basin in South Korea, is used for drinking water, fishing, irrigation, recreation, and hydroelectric power. Therefore, the water quality of the reservoir is of great importance. The main objectives of this study were to evaluate spatial and seasonal variations of surface water quality in the reservoir using multivariate statistical techniques (MSTs) along with the Trophic State Index (TSI) and Trophic State Index deviation (TSID). The empirical relationships among nutrients (total phosphorus, TP; total nitrogen, TN), chlorophyll-a (CHL-a), and annual variations of water quality parameters were also determined. To this end, 12 water quality parameters were monitored monthly at five sites along the reservoir from 1996 to 2019. Most of the parameters (all except pH, dissolved oxygen (DO), and total coliform bacteria (TCB)) showed significant spatial variations, indicating an influence of anthropogenic activities. Principal component analysis combined with factor analysis (PCA/FA) suggested that the parameters responsible for water quality variations were primarily correlated with nutrients and organic matter (anthropogenic), suspended solids (both natural and anthropogenic), and ionic concentrations (both natural and anthropogenic). Stepwise spatial discriminant analysis (DA) identified water temperature (WT), DO, electrical conductivity (EC), chemical oxygen demand (COD), the ratio of biological oxygen demand (BOD) to COD (BOD/COD), TN, TN:TP, and TCB as the parameters responsible for variations among sites, and seasonal stepwise DA identified WT, BOD, and total suspended solids (TSS) as the parameters responsible for variations among seasons. COD has increased (R2 = 0.63, p < 0.01) in the reservoir since 1996, suggesting that nonbiodegradable organic loading to the water body is rising. The empirical regression models of CHL-a-TP (R2 = 0.45) and CHL-a-TN (R2 = 0.27) indicated that TP better explained algal growth than TN. The mean TSI values for TP, CHL-a, and Secchi depth (SD) indicated a eutrophic state of the reservoir for all seasons and sites. Analysis of TSID suggested that blue-green algae dominated the algal community in the reservoir. The present results show that a significant increase in algal chlorophyll occurs during spring in the reservoir. Our findings may facilitate the management of Paldang Reservoir.

scholarly journals Influencing Factors Analysis of Taiwan Eutrophicated Reservoirs

Water ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 1325 ◽  
Author(s):  
Marsha Savira Agatha Putri ◽  
Jr-Lin Lin ◽  
Lin-Han Chiang Hsieh ◽  
Yasmin Zafirah ◽  
Gerry Andhikaputra ◽  
...  
Keyword(s):  
Water Quality ◽  
Trophic State ◽  
Oxygen Demand ◽  
Principal Component ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Total Hardness ◽  
Trophic State Index ◽  
Weather Factors ◽  
Domestic Water

Treatment cost and quality of domestic water are highly correlated with raw water quality in reservoirs. This study aims to identify the key factors that influence the trophic state levels and correlations among Carlson trophic state index (CTSI) levels, water quality parameters and weather factors in four major reservoirs in Taiwan from 2000 to 2017. Weather (e.g., air temperature, relative humidity, total precipitation, sunlight percentage and cloud cover) and water quality parameters (e.g., pH, chemical oxygen demand, suspended solids (SS), ammonia, total hardness, nitrate, nitrite and water temperature) were included in the principal component analysis and absolute principal component score models to evaluate the main governing factors of the trophic state levels (e.g., CTSI). SS were washed out by precipitation, thereby influencing the reservoir transparency tremendously and contributing over 50% to the CTSI level in eutrophicated reservoirs (e.g., the Shihmen and Chengchinghu Reservoirs). CTSI levels in the mesotrophic reservoir (e.g., Liyutan Reservoir) had strong correlation with chlorophyll-a and total phosphorus. Results show that rainfall/weather factors were the key driving factors that affected the CTSI levels in Taiwan eutrophicated reservoirs, indicating the need to consider basin management and the impacts of extreme precipitation in reservoir management and future policymaking.

scholarly journals Spatial and seasonal variations in the physicochemical parameters of water quality of a tropical reservoir, Nigeria

2021 ◽  
pp. 1160-1167
Author(s):  
L.O. Odewade ◽  
◽  
I.F. Adeniyi ◽  
A.I. Aduwo ◽  
◽  
...  
Keyword(s):  
Water Quality ◽  
Water Samples ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Sampling Station ◽  
Tropical Reservoir ◽  
Reservoir Water ◽  
Mean Values ◽  
Annual Cycles ◽  
Instrumental Methods

Abstract. In this study, the physicochemical properties of a water supply reservoir in the Esa- Odo community, Obokun Local Government Area of Osun State, Nigeria, were determined. Thirteen selected sampling stations over the three reaches of Esa-Odo reservoir along its main axis were monitored between February 2017 to December 2018 with a view to determining the seasonal and spatial variations in the general physicochemical water quality parameters of the reservoir water. From each sampling station, surface water samples were collected bi-monthly for two annual cycles (rainy and dry seasons). The collected water samples were treated and analyzed for physicochemical water quality parameters using standard instrumental and non-instrumental methods. The data obtained were analyzed using appropriate descriptive and inferential statistics. The result of the analyses of the reservoir water showed an increasing clarity from the upstream to the downstream stations with regard to water temperature, depth, transparency, turbidity, and colour. The water was generally near neutral (pH: 7.01 0.04 - 7.07 0.50) but slightly more near neutral in the rainy season than during the dry season. The water in the reservoir can be classified as a dilute salt bicarbonate freshwater with mean conductivity at the three reaches ranging from 113.39 1.67 Scm-1 to 115.24 2.46 Scm-1. Ca2+ and HCO3- were the dominant cation and anion, respectively, at all stations investigated irrespective of seasons of sampling. The mean values of most parameters determined were within permissible limits, making the river water suitable for most probable domestic and industrial uses and livestock support.

scholarly journals Study of the Water Quality of a Tropical Reservoir

2018 ◽  
Author(s):  
Alberto Quevedo-Castro ◽  
Jesús L. López ◽  
Jesus Gabriel Rangel-Peraza ◽  
Erick Bandala ◽  
Yaneth Bustos-Terrones
Keyword(s):  
Water Quality ◽  
Ecological Risk ◽  
Trophic State ◽  
Risk Index ◽  
Trophic State Index ◽  
Tropical Reservoir ◽  
State Index ◽  
Using Data

A study of the water quality of the Adolfo L&oacute;pez Mateos Reservoir (ALMD) was developed through different indicators from a spatial and seasonal perspective. Variables related to the general characteristics of water quality, trophic level and ecological risk were assessed through the water Quality Index (WQINSF-BROWN), Trophic State Index (TSICARLSON) and the Ecological Risk Index (RIHAKANSON). Using data from physical, chemical and biological parameters obtained from four sampling points in the ALMD, the water quality was assessed in each model used. The results indicated that the reservoir presents a water quality classified as &ldquo;medium&rdquo; (WQINSF-BROWN = 70), where significant variations in the concentrations of some parameters are observed. The reservoir showed a general trophic state classified as &ldquo;Mesotrophic&rdquo; (TSIGENERAL-AVERAGE = 43.04). The ecological risk analysis achieved the best classification of the methodology, discarding contamination by heavy metals in surface waters. Through this type of applied methodologies will help as decision making tools in the dam, as well as for application in other dams in the region.

scholarly journals Assessment of Temporal Variation of Water Quality Parameters and the Trophic State Index in a Subtropical Water Reservoir of Bangladesh

2021 ◽  
Vol 4 (3) ◽  
pp. 164-184
Author(s):  
Md. Sirajul Islam ◽  
Yousuf Ali ◽  
Md. Humayun Kabir ◽  
Rofi Md. Zubaer ◽  
Nowara Tamanna Meghla ◽  
...  
Keyword(s):  
Water Quality ◽  
Chlorophyll A ◽  
Trophic State ◽  
Monsoon Season ◽  
Water Reservoir ◽  
Quality Parameters ◽  
Total Alkalinity ◽  
Total Hardness ◽  
Trophic State Index ◽  
State Index

This study was conducted to determine the suitability of water quality for fisheries management in Kaptai Lake from February 2019 to January 2020. Results showed that the temperature, transparency, TDS, pH, DO, EC, alkalinity and hardness were 20.9 to 31.8°C, 17 to 303 cm, 40 to 105 mg/L, 6.82 to 7.96, 6.1 to 7.65 mg/L, 75.33 to 172.33 µS/cm, 37 to 83 mg/L and 35 to 190 mg/L, respectively. However, nutrients as NH3, NO3-, NO2-, PO43- and SO42- were 0.01 to 0.05, 0.03 to 2.21, 36 to 96, 0.01 to 0.04 and 0.3 to 1.9 mg/L, respectively. Chlorophyll a and trophic state index (TSI) were 0.70 to 2.12 µg/L and 27.43 to 37.79, respectively. Study revealed that SO42-, DO and TDS were higher than the standard of ECR. On the other hand, NH3, NO3-, NO2-, PO43-, temperature, transparency, pH, EC, total hardness, total alkalinity, Chlorophyll a and TSI were within the standard levels. Concentrations of NO3-, NO2-, PO43-, Chlorophyll a and TSI (CHL) showed no significant variation with seasons. Conversely, TDS, transparency, EC, alkalinity, hardness, and SO42- were lower in monsoon compared to pre-monsoon and post-monsoon seasons. Besides, temperature, NH3, DO and TSI (SD) were higher in monsoon season. Results concluded that the Kaptai Lake is in mesotrophic condition with TSI (CHL) less than 40, and prominently there was a positive relationship between Chlorophyll a and Trophic State Index (TSI). In this regard, major nutrients and Chlorophyll a concentration in the Kaptai Lake may have an impact on the aquatic environment.

scholarly journals Seasonal variation of plankton population of Borobila beel in Rangpur district

1970 ◽  
Vol 26 ◽  
pp. 49-54 ◽  
Author(s):  
MMR Chowdhury ◽  
MRK Mondol ◽  
C Sarker
Keyword(s):  
Water Quality ◽  
Seasonal Variation ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Total Zooplankton ◽  
Dominant Group ◽  
Mean Values ◽  
Total Phytoplankton ◽  
Two Peaks ◽  
Plankton Population

Seasonal variation of the plankton populations with some water quality parameters of Borobila beel, Rangpur district was carried out during July 2003 to June 2004. Total plankton ranged from 98.3×104 to 35.0×105 cells/l with mean values of 19.67±9.77×105 cells/l. A total of 51 genera of planktons were recorded belonging to Chlorophyceae, Bacillariophyceae, Cyanophyceae, Euglenophyceae, Dinophyceae, Crustacea and Rotifera. Among the phytoplankton, Euglenophyceae was the most dominant group and contributing 33% of total phytoplankton in Borobila beel. The greatest abundance of phytoplankton was recorded in November with an average number 28.83×105 cells/l. The minimum abundance of phytoplankton was recorded in January (61.7×104 cells/l). Among the zooplanktons Crustacea was dominant, contributing 71% of the total zooplankton population. The abundance of zooplankton showed two peaks of which one in the month of August (81.7x 104 cells/l) and another in the month of May (16.7 x104 cells/l). Phytoplankton and zooplankton have a nominal positive relationship. Zooplankton was less increased with the increasing of phytoplankton. Key words: Water quality parameters, plankton population, Borobila beel. Univ. j. zool. Rajshahi Univ. Vol. 26, 2007. pp. 49-54

scholarly journals Assessment of Water Quality Parameters Using Temporal Remote Sensing Spectral Reflectance in Arid Environments, Saudi Arabia

Water ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 556 ◽  
Author(s):  
Mohamed Elhag ◽  
Ioannis Gitas ◽  
Anas Othman ◽  
Jarbou Bahrawi ◽  
Petros Gikas
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Remote Sensing Data ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Arid Environments ◽  
Water Turbidity ◽  
Mean Values ◽  
Sensing Data ◽  
Sentinel 2

Remote sensing applications in water resources management are quite essential in watershed characterization, particularly when mega basins are under investigation. Water quality parameters help in decision making regarding the further use of water based on its quality. Water quality parameters of chlorophyll a concentration, nitrate concentration, and water turbidity were used in the current study to estimate the water quality parameters in the dam lake of Wadi Baysh, Saudi Arabia. Water quality parameters were collected daily over 2 years (2017–2018) from the water treatment station located within the dam vicinity and were correspondingly tested against remotely sensed water quality parameters. Remote sensing data were collected from Sentinel-2 sensor, European Space Agency (ESA) on a satellite temporal resolution basis. Data were pre-processed then processed to estimate the maximum chlorophyll index (MCI), green normalized difference vegetation index (GNDVI) and normalized difference turbidity index (NDTI). Zonal statistics were used to improve the regression analysis between the spatial data estimated from the remote sensing images and the nonspatial data collected from the water treatment plant. Results showed different correlation coefficients between the ground truth collected data and the corresponding indices conducted from remote sensing data. Actual chlorophyll a concentration showed high correlation with estimated MCI mean values with an R2 of 0.96, actual nitrate concentration showed high correlation with the estimated GNDVI mean values with an R2 of 0.94, and the actual water turbidity measurements showed high correlation with the estimated NDTI mean values with an R2 of 0.94. The research findings support the use of remote sensing data of Sentinel-2 to estimate water quality parameters in arid environments.

scholarly journals Online Measurement Method of Water Quality in the Sulejow Reservoir

2018 ◽  
Vol 25 (1) ◽  
pp. 89-100 ◽  
Author(s):  
Aleksandra Ziemińska-Stolarska ◽  
Janusz Adamiec ◽  
Mirosław Imbierowicz ◽  
Ewa Imbierowicz ◽  
Marcin Jaskulski ◽  
...  
Keyword(s):  
Water Quality ◽  
Statistical Tests ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Ion Concentration ◽  
Ammonium Ion ◽  
Vast Number ◽  
Mean Values ◽  
Blue Green Algae ◽  
Mobile Measurements

Abstract The paper presents methodology of accurate mobile measurements of water quality parameters such as temperature, dissolved oxygen, chlorophyll “a” concentration, ammonium ion concentration, conductivity, pH and blue-green algae content in water. The measurements (probe EXO 2, YSI, USA) were made on various depths of probe immersion (1.5, 2.5 and 3.5 m) and at different towing speeds of the probe (approx. 5.4 and 9.0 km/h). Static measurements carried out on the same route provided reference values for the measurements in motion to compare the repeatability of static and mobile methods. The tests were also evaluated by observation of probe behavior in motion, e.g. water disturbance intensity, access of light (sun rays) to the sensors. Statistical tests confirmed that the mean values of water quality parameters from mobile measurements with the speed of 5.4 km/h at the depth 1.5 m does not differ from the stationary measurements. Results of statistical analysis prove that water quality parameters can be measured accurately keeping established speed of towing the probe at the fixed depth. Methodology of mobile measurements elaborated in the frame of this work allows to collect vast number of data which can be used to obtain GIS point maps of water quality parameters in large water bodies.

scholarly journals Retrieval of Water Quality Parameters from Hyperspectral Images Using Hybrid Bayesian Probabilistic Neural Network

2020 ◽  
Vol 12 (10) ◽  
pp. 1567
Author(s):  
Yishan Zhang ◽  
Lun Wu ◽  
Huazhong Ren ◽  
Licui Deng ◽  
Pengcheng Zhang
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Chlorophyll A ◽  
Probabilistic Neural Network ◽  
Oxygen Demand ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Bayesian Probability ◽  
Proposed Model ◽  
Probability Methods

The protection of water resources is of paramount importance to human beings’ practical lives. Monitoring and improving water quality nowadays has become an important topic. In this study, a novel Bayesian probabilistic neural network (BPNN) improved from ordinary Bayesian probability methods has been developed to quantitatively predict water quality parameters including phosphorus, nitrogen, chemical oxygen demand (COD), biochemical oxygen demand (BOD), and chlorophyll a. The proposed method, based on conventional Bayesian probability methods, involves feature engineering and deep neural networks. Additionally, it extracts significant information for each endmember from combinations of spectra by feature extraction, with spectral unmixing based on mathematical and statistical analysis, and calculates each of the water quality parameters. The experimental results show the great performance of the proposed model with all coefficient of determination R 2 over 0.9 greater than the values (0.6–0.8) from conventional methods, which are greater than ordinary Bayesian probability analysis. The mean percent of absolute error (MPAE) is taken into account as an important statistical criterion to evaluate model performance, and our results show that MPAE ranges from 4% (nitrogen) to 10% (COD). The root mean squared errors (RMSEs) of phosphorus, nitrogen, COD, BOD, and chlorophyll-a (Chla) are 0.03 mg/L, 0.28 mg/L, 3.28 mg/L, 0.49 mg/L, and 0.75 μg/L, respectively. In comparison with other deep learning methods, this study takes a relatively small amount of data as training data to train the proposed model and the proposed model is then tested on the same amount of testing data, achieving a greater performance. Thus, the proposed method is time-saving and more effective. This study proposes a more compatible and effective method to assist with decomposing combinations of hyperspectral signatures in order to calculate the content level of each water quality parameter. Moreover, the proposed method is practically applied to hyperspectral image data on board an unmanned aerial vehicle in order to monitor the water quality on a large scale and trace the location of pollution sources in the Maozhou River, Guangdong Province of China, obtaining well-explained and significant results.

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