scholarly journals An interactive data management system for river water quality data

1982 ◽  
Vol 16 (1) ◽  
pp. 31-36 ◽  
Author(s):  
Rolf A. Deininger
Keyword(s):  
Water Quality ◽  
Data Management ◽  
River Water ◽  
Management System ◽  
River Water Quality ◽  
Data Management System ◽  
Quality Data ◽  
Water Quality Data ◽  
Interactive Data

Implementation of a large scale water quality data management system

1976 ◽  
Vol 10 (1) ◽  
pp. 31-36 ◽  
Author(s):  
William D. Haseman ◽  
Clyde Holsapple ◽  
Andrew B. Whinston
Keyword(s):  
Water Quality ◽  
Data Management ◽  
Management System ◽  
Large Scale ◽  
Data Management System ◽  
Quality Data ◽  
Water Quality Data

scholarly journals PENGEMBANGAN SISTEM PENGELOLAAN DATA KUALITAS AIR LIMBAH DOMESTIK PERKOTAAN

2018 ◽  
Vol 5 (1) ◽  
Author(s):  
Heru Dwi Wahjono
Keyword(s):  
Waste Water ◽  
Water Quality ◽  
Data Management ◽  
River Water ◽  
Urban Areas ◽  
Data Management System ◽  
Quality Data ◽  
Domestic Waste ◽  
Water Quality Data ◽  
The Government

One of the activities undertaken within the framework of environmental sanitation management by the government is to publish a variety of regulations governing the wastewater disposal problem. Sources of pollution of the most dominant in urban areas are domestic wastewater. Government institutions through BPLHD have conducted regular monitoring of river water quality in Jakarta. Management of water quality data from domestic waste sources is needed to control river water pollution. This requires data management system of urban domestic waste water quality. In this article will be discussed the development of data management systems for domestic waste water quality with reference to the current applicable regulations. Key words : water quality database,  domestic waste water quality

scholarly journals GRQA: Global River Water Quality Archive

2021 ◽  
Vol 13 (12) ◽  
pp. 5483-5507
Author(s):  
Holger Virro ◽  
Giuseppe Amatulli ◽  
Alexander Kmoch ◽  
Longzhu Shen ◽  
Evelyn Uuemaa
Keyword(s):  
Water Quality ◽  
Time Series ◽  
River Water ◽  
Environmental Sustainability ◽  
Large Scale ◽  
Observation Time ◽  
River Water Quality ◽  
Quality Data ◽  
Observation Data ◽  
Water Quality Data

Abstract. Large-scale hydrological studies are often limited by the lack of available observation data with a good spatiotemporal coverage. This has affected the reproducibility of previous studies and the potential improvement of existing hydrological models. In addition to the observation data themselves, insufficient or poor-quality metadata have also discouraged researchers from integrating the already-available datasets. Therefore, improving both the availability and quality of open water quality data would increase the potential to implement predictive modeling on a global scale. The Global River Water Quality Archive (GRQA) aims to contribute to improving water quality data coverage by aggregating and harmonizing five national, continental and global datasets: CESI (Canadian Environmental Sustainability Indicators program), GEMStat (Global Freshwater Quality Database), GLORICH (GLObal RIver CHemistry), Waterbase and WQP (Water Quality Portal). The GRQA compilation involved converting observation data from the five sources into a common format and harmonizing the corresponding metadata, flagging outliers, calculating time series characteristics and detecting duplicate observations from sources with a spatial overlap. The final dataset extends the spatial and temporal coverage of previously available water quality data and contains 42 parameters and over 17 million measurements around the globe covering the 1898–2020 time period. Metadata in the form of statistical tables, maps and figures are provided along with observation time series. The GRQA dataset, supplementary metadata and figures are available for download on the DataCite- and OpenAIRE-enabled Zenodo repository at https://doi.org/10.5281/zenodo.5097436 (Virro et al., 2021).

scholarly journals STATUS MUTU DAERAH PENAMBANGAN PASIR DI PERAIRAN SUNGAI SERAYU DENGAN MENGGUNAKAN METODE STORET

INFO-TEKNIK ◽  
2018 ◽  
Vol 19 (2) ◽  
pp. 155
Author(s):  
Nurlinda Ayu Triwuri ◽  
Murni Handayani ◽  
Rosita Dwityaningsih
Keyword(s):  
Water Quality ◽  
River Water ◽  
Good Condition ◽  
River Water Quality ◽  
Quality Data ◽  
Quality Level ◽  
Sand Mining ◽  
Water Quality Data ◽  
Group D

The quality of river water is strongly related to human activities in it. Changesin the condition of water quality in the river flow are the effects of the dischargefrom existing land use. One of them is sand mining activities along the SerayuRiver, especially around Tumiyang, Kebasen, Banyumas Regency. Activitiesfrom sand mining will cause a decrease in river water quality. From this activity,it is necessary to study the status of water quality using the STORET method todetermine the quality of river water so that the river can be utilized in accordancewith the applicable designation.The STORET method is one method for determining water quality data withwater quality standards in accordance with the appointment of Minister ofEnvironment Decree No.115 2003. This research is a quantitative descriptivestudy to determine the water quality of the Serayu river in the sand of miningareas precisely in Banyumas Regency. The parameters measured in this studywere measurements of Total Disolved Solid (TDS), temperature, pH, andElectrical Conductivity. Determining the location of taking water using apurposive sampling method.Based on the results of data analysis using the Storet method and refers to thequality standards of Government Regulation No.20 of 1990 Group D. Waterquality in Serayu River has a total score of 9 after sand mining. This shows thequality status of the lightly polluted Serayu river (-1 to -10). But still in class Band the river water quality level is still in good condition. There are temperatureparameters that exceed the threshold of 25 - 32oC, but the TDS, DHL and pHparameters are still within the threshold of designation in Group D.

scholarly journals Investigation of the Soan River Water Quality Using Multivariate Statistical Approach

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Zakaullah ◽  
Naeem Ejaz
Keyword(s):  
Water Quality ◽  
River Water ◽  
Water Quality Management ◽  
Oxygen Demand ◽  
River Water Quality ◽  
Quality Data ◽  
Spatiotemporal Pattern ◽  
Multivariate Statistical ◽  
Water Quality Data

Evaluating the quality of river water is a critical process due to pollution and variations of natural or anthropogenic origin. For the Soan River (Pakistan), seven sampling sites were selected in the urban area of Rawalpindi/Islamabad, and 18 major chemical parameters were examined over two seasons, i.e., premonsoon and postmonsoon 2019. Multivariate statistical approaches such as the Spearman correlation coefficient, cluster analysis (CA), and principal component analysis (PCA) were used to evaluate the water quality of the Soan River based on temporal and spatial patterns. Analytical results obtained by PCA show that 92.46% of the total variation in the premonsoon season and 93.11% in the postmonsoon season were observed by only two loading factors in both seasons. The PCA and CA made it possible to extract and recognize the origins of the factors responsible for water quality variations during the year 2019. The sampling stations were grouped into specific clusters on the basis of the spatiotemporal pattern of water quality data. The parameters dissolved oxygen (DO), biochemical oxygen demand (BOD), chemical oxygen demand (COD), turbidity, and total suspended solids (TSS) are among the prominent contributing variations in water quality, indicating that the water quality of the Soan River deteriorates gradually as it passes through the urban areas, receiving domestic and industrial wastewater from the outfalls. This study indicates that the adopted methodology can be utilized effectively for effective river water quality management.

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