scholarly journals Remote Sensing of Water Quality Parameters over Lake Balaton by Using Sentinel-3 OLCI

Water ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 1428 ◽  
Author(s):  
Katalin Blix ◽  
Károly Pálffy ◽  
Viktor Tóth ◽  
Torbjørn Eltoft
Keyword(s):  
Water Quality ◽  
Regression Model ◽  
Gaussian Process ◽  
Gaussian Process Regression ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Water Quality Parameter ◽  
Aquatic Environments ◽  
Lake Balaton ◽  
Spectral Bands

The Ocean and Land Color Instrument (OLCI) onboard Sentinel 3A satellite was launched in February 2016. Level 2 (L2) products have been available for the public since July 2017. OLCI provides the possibility to monitor aquatic environments on 300 m spatial resolution on 9 spectral bands, which allows to retrieve detailed information about the water quality of various type of waters. It has only been a short time since L2 data became accessible, therefore validation of these products from different aquatic environments are required. In this work we study the possibility to use S3 OLCI L2 products to monitor an optically highly complex shallow lake. We test S3 OLCI-derived Chlorophyll-a (Chl-a), Colored Dissolved Organic Matter (CDOM) and Total Suspended Matter (TSM) for complex waters against in situ measurements over Lake Balaton in 2017. In addition, we tested the machine learning Gaussian process regression model, trained locally as a potential candidate to retrieve water quality parameters. We applied the automatic model selection algorithm to select the combination and number of spectral bands for the given water quality parameter to train the Gaussian Process Regression model. Lake Balaton represents different types of aquatic environments (eutrophic, mesotrophic and oligotrophic), hence being able to establish a model to monitor water quality by using S3 OLCI products might allow the generalization of the methodology.

scholarly journals Assessment of Physicochemical Water Quality Parameters and Heavy Metals Concentration in Water Samples from Buriganga River Adjacent to Dhaka City

2017 ◽  
Vol 9 (2) ◽  
pp. 97-104
Author(s):  
MMM Hoque ◽  
PP Deb
Keyword(s):  
Heavy Metals ◽  
Water Quality ◽  
River Water ◽  
Water Samples ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Water Quality Parameter ◽  
Dhaka City ◽  
Standard Level ◽  
Buriganga River

This study was conducted to know the status of physicochemical water quality parameter and heavy metal concentration in the water of Buriganga river, adjoining to Dhaka city. Water samples were collected from five different points of Buriganga river and were analyzed to determine pH, electrical conductivity (EC), total dissolved solids (TDS), dissolved oxygen (DO), biological oxygen demand (BOD), chromium (Cr), lead (Pb), cadmium (Cd), copper (Cu) and manganese (Mn) content. Most of the measured water quality parameters and concentration of heavy metals were exceeded the standard level set by ECR and ADB. Among heavy metals concentration, level of chromium and cadmium were 4-5 times higher than the standard drinking level, these results indicate that surrounding industrial wastewater discharging from textile and tannery industries, which pollute the Buriganga river water. During the observation, at Hazaribagh station BOD level was found 32 times higher than drinking water standard level and 6 times higher than standard irrigation level, indicating Buriganga river water is extremely polluted by microorganism and is not suitable for household and irrigational use. Similarly, DO level at Buriganga river water was 5 times lower than the standard level, which indicates that Buriganga river water is extremely polluted and is unsuitable for aquatic life which are dependent on DO for their sustain. In the present study, the measured level of EC, chromium, cadmium and copper were found higher level as compare to the previous studies.J. Environ. Sci. & Natural Resources, 9(2): 97-104 2016

scholarly journals Performance of landscape composition metrics for predicting water quality in headwater catchments

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Linda R. Staponites ◽  
Vojtěch Barták ◽  
Michal Bílý ◽  
Ondřej P. Simon
Keyword(s):  
Water Quality ◽  
Land Use ◽  
Management Strategies ◽  
Ecological Integrity ◽  
Quality Parameters ◽  
Landscape Composition ◽  
Water Quality Parameters ◽  
Distance Measures ◽  
Water Quality Parameter ◽  
Headwater Catchments

Abstract Land use is a predominant threat to the ecological integrity of streams and rivers. Understanding land use-water quality interactions is essential for the development and prioritization of management strategies and, thus, the improvement of water quality. Weighting schemes for land use have recently been employed as methods to advance the predictive power of empirical models, however, their performance has seldom been explored for various water quality parameters. In this work, multiple landscape composition metrics were applied within headwater catchments of Central Europe to investigate how weighting land use with certain combinations of spatial and topographic variables, while implementing alternate distance measures and functions, can influence predictions of water quality. The predictive ability of metrics was evaluated for eleven water quality parameters using linear regression. Results indicate that stream proximity, measured with Euclidean distance, in combination with slope or log-transformed flow accumulation were dominant factors affecting the concentrations of pH, total phosphorus, nitrite and orthophosphate phosphorus, whereas the unweighted land use composition was the most effective predictor of calcium, electrical conductivity, nitrates and total suspended solids. Therefore, both metrics are recommended when examining land use-water quality relationships in small, submontane catchments and should be applied according to individual water quality parameter.

scholarly journals Comparing PlanetScope to Landsat-8 and Sentinel-2 for Sensing Water Quality in Reservoirs in Agricultural Watersheds

2021 ◽  
Vol 13 (9) ◽  
pp. 1847
Author(s):  
Abubakarr S. Mansaray ◽  
Andrew R. Dzialowski ◽  
Meghan E. Martin ◽  
Kevin L. Wagner ◽  
Hamed Gholizadeh ◽  
...  
Keyword(s):  
Water Quality ◽  
Chlorophyll A ◽  
Water Quality Monitoring ◽  
Agricultural Runoff ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Quality Monitoring ◽  
Landsat 8 ◽  
Spectral Bands ◽  
Sentinel 2

Agricultural runoff transports sediments and nutrients that deteriorate water quality erratically, posing a challenge to ground-based monitoring. Satellites provide data at spatial-temporal scales that can be used for water quality monitoring. PlanetScope nanosatellites have spatial (3 m) and temporal (daily) resolutions that may help improve water quality monitoring compared to coarser-resolution satellites. This work compared PlanetScope to Landsat-8 and Sentinel-2 in their ability to detect key water quality parameters. Spectral bands of each satellite were regressed against chlorophyll a, turbidity, and Secchi depth data from 13 reservoirs in Oklahoma over three years (2017–2020). We developed significant regression models for each satellite. Landsat-8 and Sentinel-2 explained more variation in chlorophyll a than PlanetScope, likely because they have more spectral bands. PlanetScope and Sentinel-2 explained relatively similar amounts of variations in turbidity and Secchi Disk data, while Landsat-8 explained less variation in these parameters. Since PlanetScope is a commercial satellite, its application may be limited to cases where the application of coarser-resolution satellites is not feasible. We identified scenarios where PS may be more beneficial than Landsat-8 and Sentinel-2. These include measuring water quality parameters that vary daily, in small ponds and narrow coves of reservoirs, and at reservoir edges.

scholarly journals The Effect of Alterations in Water Quality Parameters on the Occurrence of Bacterial Diseases in Different Aquatic Environments

2021 ◽  
Vol 9 (12) ◽  
Author(s):  
Hanan S. Khalefa ◽  
Dalia A. Abdel-Moneam ◽  
Elshaimaa Ismael ◽  
Mahmoud Mostafa Fathy Waziry ◽  
Mennaallah Samir Gaber Ali ◽  
...  
Keyword(s):  
Water Quality ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Aquatic Environments ◽  
Bacterial Diseases

scholarly journals Computational and Numerical Modeling for Classification of Water Quality of Lake

2020 ◽  
Vol 11 (9) ◽  
pp. 425-431
Author(s):  
Jonalyn G. Ebron ◽  
◽  
Rommel Ivan D. De Leon ◽  
Arviejhay D. Alejandro ◽  
Basaron A. Amoranto
Keyword(s):  
Water Quality ◽  
Linear Regression ◽  
Water Quality Monitoring ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Water Quality Parameter ◽  
Support Vector ◽  
Poor Correlation ◽  
Study Results

In this study, the Multivariate Linear Regression (MLR), Artificial Neural Network (ANN), k-Nearest Neighbour (kNN), and Support Vector Machine (SVM) models had been developed to simulate and to predict the water quality of Laguna Lake. The input variables for the MLR model had been determined through linear regression. The ANN, kNN, and SVM had been modelled per water quality parameter with cross validation and evaluated through its accuracy. The performance of the MLR models had been evaluated with the statistical metrics R-squared, Mean Absolute Error, and Root Mean Square Error. A web-based water quality monitoring had been developed to incorporate in their monitoring. The results had indicated that the performance of SVM is superior in the prediction of classes in most water quality parameters. The study results had shown that the poor correlation between the water quality parameters indicated that the data cannot be modelled. The results had shown that the correlation had not reached the threshold to be significant of 60% for R-squared. As per the classification models, the results of the comparison had shown that SVM had been the best model in the majority of parameters.

scholarly journals Dynamic model of dissolved oxygen in intensive concrete pond of white leg shrimp (Litopenaeus vannamei) in Bomo Village, East Java

2021 ◽  
Vol 919 (1) ◽  
pp. 012058
Author(s):  
Supriatna ◽  
M Mahmudi
Keyword(s):  
Water Quality ◽  
Dissolved Oxygen ◽  
Regression Model ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Total Alkalinity ◽  
Oxygen Solubility ◽  
Shrimp Culture ◽  
Shrimp Ponds ◽  
Total Bacteria

Abstract This study is to understand a simple model of dissolved oxygen (DO) and other water quality factors that affect it in two seasons in intensive white leg shrimp ponds. Water quality parameters in the dry and rainy seasons in several ponds were sampled daily, including temperature, pH, (DO), salinity, twice a week, including ammonium, nitrite, nitrate, orthophosphate, total alkalinity, and total bacteria. Besides daily, dissolved oxygen is also measured before the harvest every two hours by using dark bottles and light bottles. Pond water quality parameters are still suitable for white shrimp culture. Daily DO shrimp ponds form a polynomial regression model. DO in light bottles constructed a quadratic regression model, DO in dark bottles created a linear regression pattern, with a DO reduction rate of 0.6338 mg−l per hour. During one of the shrimp cultures, the DO model showed an inverse quadratic equation with the lowest oxygen solubility level on day 57. DO was positively correlated with changes in salinity and transparency and negatively related to ammonium, nitrate, phosphate, total alkalinity, and total bacteria count.

scholarly journals Analisis kelayakan lahan budi daya rumput laut (Ulva sp.) pada lokasi rencana pengembangan North Sulawesi Marine Education Center di Likupang Timur

2017 ◽  
Vol 5 (3) ◽  
Author(s):  
Selvanus Edy ◽  
Edwin L. A. Ngangi ◽  
Joppy D. Mudeng
Keyword(s):  
Water Quality ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Water Quality Parameter ◽  
Seaweed Cultivation ◽  
Field Station ◽  
North Sulawesi ◽  
Marine Education ◽  
Development Area

The purpose of this research was to know and evaluate the condition of aquatic environment and water quality parameters for cultivation of seaweed Ulva sp. This research was conducted on North Sulawesi Marine Education Center (NSMEC). NSMEC is planned to be built at Marine Field Station FPIK UNSRAT located in Likupang Timur Kabupaten Minahasa Utara. The waters of NSMEC development area are geographically located at 1040.437 'LU and 12504.499' BT. The determination of 4 stations was done by purposive sampling which was considered to represent the condition of waters. Coordinate stations were recorded with GPS help. The data were collected for 14 days every 6:00 pm, at 12:00 pm and 17:00 pm. The observation of environmental conditions was carried out for protection factor and substrate of water base, while water quality parameter measured in situ included depth, brightness, temperature, dissolved oxygen (DO) salinity, pH and current velocity. Phosphate, nitrate and total suspended solid (TSS) were measured. Tide measurements were measured every hour for 24 hours. Data analysis used conformity matrices that included scores and weights for the determination of conformity classes. Class suitability was used to describe the suitability of seaweed. The results showed that the waters of the NSMEC development zone were in class S1 meaning very suitable for seaweed cultivation location. The analysis results of each station found that 4 stations were very suitable. As conclusion, water environment and water quality parameters of the waters of North Sulawesi Marine Education Center development area located at Marine Field Station FPIK UNSRAT were categorized as very suitable for seaweed cultivation, Ulva sp.Keywords: Ulva sp., feasibility analysis, water quality, location suitability

scholarly journals Optical Water Type Guided Approach to Estimate Optical Water Quality Parameters

2020 ◽  
Vol 12 (6) ◽  
pp. 931 ◽  
Author(s):  
Kristi Uudeberg ◽  
Age Aavaste ◽  
Kerttu-Liis Kõks ◽  
Ave Ansper ◽  
Mirjam Uusõue ◽  
...  
Keyword(s):  
Water Quality ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Water Quality Parameter ◽  
Water Type ◽  
Secchi Disk ◽  
Secchi Disk Depth ◽  
Chl A ◽  
Monitoring Programs

Currently, water monitoring programs are mainly based on in situ measurements; however, this approach is time-consuming, expensive, and may not reflect the status of the whole water body. The availability of Multispectral Imager (MSI) and Ocean and Land Colour Instrument (OLCI) free data with high spectral, spatial, and temporal resolution has increased the potential of adding remote sensing techniques into monitoring programs, leading to improvement of the quality of monitoring water. This study introduced an optical water type guided approach for boreal regions inland and coastal waters to estimate optical water quality parameters, such as the concentration of chlorophyll-a (Chl-a) and total suspended matter (TSM), the absorption coefficient of coloured dissolved organic matter at a wavelength of 442 nm (aCDOM(442)), and the Secchi disk depth, from hyperspectral, OLCI, and MSI reflectance data. This study was based on data from 51 Estonian and Finnish lakes and from the Baltic Sea coastal area, which altogether were used in 415 in situ measurement stations and covered a wide range of optical water quality parameters (Chl-a: 0.5–215.2 mg·m−3; TSM: 0.6–46.0 mg·L−1; aCDOM(442): 0.4–43.7 m−1; and Secchi disk depth: 0.2–12.2 m). For retrieving optical water quality parameters from reflectance spectra, we tested 132 empirical algorithms. The study results describe the best algorithm for each optical water type for each spectral range and for each optical water quality parameter. The correlation was high, from 0.87 up to 0.93, between the in situ measured optical water quality parameters and the parameters predicted by the optical water type guided approach.

scholarly journals The Monitoring of Black-Odor River by Electronic Nose with Chemometrics for pH, COD, TN, and TP

Chemosensors ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 168
Author(s):  
Shanshan Qiu ◽  
Pingzhi Hou ◽  
Jingang Huang ◽  
Wei Han ◽  
Zhiwei Kang
Keyword(s):  
Water Quality ◽  
Electronic Nose ◽  
Detection System ◽  
Oxygen Demand ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Water Quality Parameter ◽  
Least Squares Regression ◽  
Severe Problem ◽  
Linear Discriminant

Black-odor rivers are polluted urban rivers that often are black in color and emit a foul odor. They are a severe problem in aquatic systems because they can negatively impact the living conditions of residents and the functioning of ecosystems and local economies. Therefore, it is crucial to identify ways to mitigate the water quality parameters that characterize black-odor rivers. In this study, we tested the efficacy of an electronic nose (E-nose), which was inexpensive, fast, and easy to operate, for qualitative recognition analysis and quantitative parameter prediction of samples collected from the Yueliang River in Huzhou City. The E-nose sensors were cross-sensitive to the volatile compounds in black-odor water. The device recognized the samples from different river sites with 100% accuracy based on linear discriminant analysis. For water quality parameter predictions, partial least squares regression models based on E-nose signals were established, and the coefficients between the actual water quality parameters (pH, chemical oxygen demand, total nitrogen content, and total phosphorous content) and the predicted values were very high (R2 > 0.90) both in the training and testing sets. These results indicate that E-nose technology can be a fast, easy-to-build, and cost-effective detection system for black-odor river monitoring.

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