scholarly journals Multispectral Remote Sensing Utilization for Monitoring Chlorophyll-a Levels in Inland Water Bodies in Jordan

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Nidal M. Hussein ◽  
Mohammed N. Assaf
Keyword(s):  
Water Quality ◽  
Water Samples ◽  
Cost Effective ◽  
Water Quality Monitoring ◽  
Predictive Capability ◽  
Inland Water ◽  
Chl A ◽  
Multispectral Satellite Images ◽  
Inland Water Body ◽  
Future Work

This study focuses on the utilization of multispectral satellite images for remote water-quality evaluation of inland water body in Jordan. The geophysical parameters based on water’s optical properties, due to the presence of optically active constituents, are used to determine contaminant level in water. It has a great potential to be employed for continuous and cost-effective water-quality monitoring and leads to a reliable regularly updated tool for better water sector management. Three sets of water samples were collected from three different dams in Jordan. Chl-a concentration of the water samples was measured and used with corresponding Sentinel 2 surface reflectance (SR) data to develop a predictive model. Chl-a concentrations and corresponding SR data were used to calibrate and validate different models. The predictive capability of each of the investigated models was determined in terms of determination coefficient (R2) and lowest root mean square error (RMSE) values. For the investigated sites, the B3/B2 (green/blue bands) model and the Ln (B3/B2) model showed the best overall predictive capability of all models with the highest R2 and the lowest RMSE values of (0.859, 0.824) and (30.756 mg/m3, 29.787 mg/m3), respectively. The outcome of this study on selected sites can be expanded for future work to cover more sites in the future and ultimately cover all sites in Jordan.

Pesticide Contamination of Groundwater in Virginia: BMP Impact Assessment

1993 ◽  
Vol 28 (3-5) ◽  
pp. 379-387 ◽  
Author(s):  
S. Mostaghimi ◽  
P. W. McClellan ◽  
R. A. Cooke
Keyword(s):  
Water Quality ◽  
Land Use ◽  
Best Management Practices ◽  
Water Samples ◽  
Management Practices ◽  
Water Quality Monitoring ◽  
Pesticide Contamination ◽  
Cropping Patterns ◽  
Creek Watershed ◽  
Surface And Groundwater

The Nomini Creek Watershed/Water Quality monitoring project was initiated in 1985, as part of the Chesapeake Bay Agreement of 1983, to quantify the impacts of agricultural best management practices (BMPs) on improving water quality. The watershed monitoring system was designed to provide a comprehensive assessment of the quality of surface and groundwater as influenced by changes in land use, agronomic, and cultural practices in the watershed over the duration of the project. The primary chemical characteristics monitored include both soluble and sediment-bound nutrients and pesticides in surface and groundwater. Water samples from 8 monitoring wells located in agricultural areas in the watershed were analyzed for 22 pesticides. A total of 20 pesticides have been detected in water samples collected. Atrazine is the most frequently detected pesticide. Detected concentrations of atrazine ranged from 0.03 - 25.56 ppb and occurred in about 26 percent of the samples. Other pesticides were detected at frequencies ranging from 1.6 to 14.2 percent of all samples collected and concentrations between 0.01 and 41.89 ppb. The observed concentrations and spatial distributions of pesticide contamination of groundwater are compared to land use and cropping patterns. Results indicate that BMPs are quite effective in reducing pesticide concentrations in groundwater.

scholarly journals Integration of Remote Sensing and Mexican Water Quality Monitoring System Using an Extreme Learning Machine

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4118
Author(s):  
Leonardo F. Arias-Rodriguez ◽  
Zheng Duan ◽  
José de Jesús Díaz-Torres ◽  
Mónica Basilio Hazas ◽  
Jingshui Huang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Extreme Learning Machine ◽  
Monitoring System ◽  
Field Measurements ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Chl A ◽  
Learning Machine ◽  
Level 2

Remote Sensing, as a driver for water management decisions, needs further integration with monitoring water quality programs, especially in developing countries. Moreover, usage of remote sensing approaches has not been broadly applied in monitoring routines. Therefore, it is necessary to assess the efficacy of available sensors to complement the often limited field measurements from such programs and build models that support monitoring tasks. Here, we integrate field measurements (2013–2019) from the Mexican national water quality monitoring system (RNMCA) with data from Landsat-8 OLI, Sentinel-3 OLCI, and Sentinel-2 MSI to train an extreme learning machine (ELM), a support vector regression (SVR) and a linear regression (LR) for estimating Chlorophyll-a (Chl-a), Turbidity, Total Suspended Matter (TSM) and Secchi Disk Depth (SDD). Additionally, OLCI Level-2 Products for Chl-a and TSM are compared against the RNMCA data. We observed that OLCI Level-2 Products are poorly correlated with the RNMCA data and it is not feasible to rely only on them to support monitoring operations. However, OLCI atmospherically corrected data is useful to develop accurate models using an ELM, particularly for Turbidity (R2=0.7). We conclude that remote sensing is useful to support monitoring systems tasks, and its progressive integration will improve the quality of water quality monitoring programs.

Inland water quality monitoring in Australia

2013 ◽  
Author(s):  
Tim J. Malthus ◽  
Erin L. Hestir ◽  
Arnold Dekker ◽  
Janet Anstee ◽  
Hannelie Botha ◽  
...  
Keyword(s):  
Water Quality ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Inland Water

scholarly journals Genotyping and phylogenetic analysis of free-living amoeba (Acanthamoeba and Naegleria) in treated and untreated water in the northeastern provinces of Iran

2021 ◽  
Author(s):  
Yousef Sharifi ◽  
Omid Ahmadi ◽  
Bibi Razieh Hossini Farash ◽  
Nazgol Khosravinia ◽  
Reza Fotouhi-Ardakani ◽  
...  
Keyword(s):  
Water Quality ◽  
Water Samples ◽  
Taxonomic Status ◽  
Water Quality Monitoring ◽  
Pcr Analysis ◽  
Growth Media ◽  
Genotype Distribution ◽  
Free Living ◽  
Northern Iran ◽  
Pcr Products

Abstract Free-living amoebae (FLA) are widely distributed protozoa in natural or man-made aquatic environments without the need for a host organism for survival. Several strains of FLA are known to be pathogenic. As of date, there is inadequate data on the geographical distribution of FLA in northeastern and northern Iran. This study aimed to investigate the prevalence and genotype distribution of Acanthamoeba and Naegleria in drinking water and surface water samples in northern and northeastern Iran. A total of 60 water samples were collected and filtered from various sources for the presence of amoebae. DNA extraction was performed, and PCR confirmed the presence of FLA. PCR products were sequenced to identify the species/genotype. Phylogenetic relationships and taxonomic status constructed using MEGA X software. The findings on growth media showed 35% (21/60) and 26% (16/60) were positive for Acanthamoeba and Naegleria, respectively, while PCR analysis also obtained similar results. All isolates of Acanthamoeba were identified as T4 genotype. Poor water quality, as well as insufficient preservation and treatment, might indicate that chlorine disinfection is ineffective in removing contamination of amoebas in treated water samples. Therefore, regular water quality monitoring is essential to control amoeba's growth, reducing the risk of human infections with FLA.

Development of Multiple Water Quality Measurement Devices

2011 ◽  
Vol 695 ◽  
pp. 606-609
Author(s):  
Pill Jae Kwak ◽  
Seog Ku Kim ◽  
Sang Leen Yun ◽  
Sung Won Kang ◽  
Hyun Dong Lee ◽  
...  
Keyword(s):  
Water Quality ◽  
Optical Sensors ◽  
Quality Measurement ◽  
Cost Effective ◽  
Water Quality Monitoring ◽  
In Situ Monitoring ◽  
Quality Monitoring ◽  
Temperature Conductivity ◽  
High Data ◽  
Water Quality Measurement

The water quality measurement device that we developed measures pH, water temperature, conductivity, dissolved oxygen, turbidity and nitrate. And it measures all parameters simultaneously. The water resistant and screw packing technology also applied for improved mechanical reliability during water quality monitoring. A comparison between the performances of major company products (YSI, Hydrolab etc.) and this device don't provide a stark contrast. This device was verified through the KOREA’s Environmental Examination Methods. This device is offered reliable and cost-effective water quality monitoring solutions. Upgrades will be available and will include the technologies that are self-cleaning optical sensors with integrated wipers remove biofouling and maintain high data accuracy and optimal power management and built-in battery compartment extends in situ monitoring periods.

A rapid, cost-effective screening tool for measuring Chl-a in water samples

2017 ◽  
Vol 33 (3) ◽  
pp. 217-222
Author(s):  
Tiffany Trent ◽  
John Hendrickson ◽  
Matthew C. Harwell
Keyword(s):  
Water Samples ◽  
Screening Tool ◽  
Cost Effective ◽  
Effective Screening ◽  
Chl A
Sign in / Sign up

Export Citation Format

Share Document