A summary of river water quality data collected within the Land–Ocean Interaction Study: core data for eastern UK rivers draining to the North Sea

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).

Download Full-text

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

INFO-TEKNIK ◽

10.20527/jit.v19i2.150 ◽

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.

Download Full-text

Investigation of the Soan River Water Quality Using Multivariate Statistical Approach

International Journal of Photoenergy ◽

10.1155/2020/6644796 ◽

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.

Download Full-text

Flow Stratification of River Water Quality Data to Elucidate Nutrient Transfer Pathways in Mesoscale Catchments

Transactions of the ASABE ◽

10.13031/trans.59.11145 ◽

2016 ◽

Vol 59 (2) ◽

pp. 545-551 ◽

Cited By ~ 5

Keyword(s):

Water Quality ◽

River Water ◽

River Water Quality ◽

Quality Data ◽

Nutrient Transfer ◽

Water Quality Data

Download Full-text

Novel bacterial ratio for predicting faecal age

Water Science & Technology ◽

10.2166/wst.2003.0157 ◽

2003 ◽

Vol 47 (3) ◽

pp. 45-49 ◽

Cited By ~ 6

Author(s):

J. Nieman ◽

G.M. Brion

Keyword(s):

Water Quality ◽

River Water ◽

Total Coliform ◽

River Water Quality ◽

Low Flow ◽

Quality Data ◽

Water Quality Data ◽

Ongoing Research ◽

Kentucky River ◽

Survival Studies

This study presents an extension of ongoing research into the utility of the ratio of colonies isolated on membrane filters during the total coliform test using m-Endo broth media. Investigations into the relative shifts in concentrations of indicator bacterial populations over time, in laboratory-based survival studies conducted with filtered river water, were undertaken. Also, analysis of Kentucky River water quality data collected from the inlet of a local water treatment plant was carried out. Survival studies found that the ratio between the raw concentrations of atypical colonies (AC) and total coliform colonies (TC) was directly related to the amount of time coliform spiked river water had been held in open jars in the laboratory. The AC/TC ratio in the jars would rise from <1 at the time of coliform spiking to >200 within 4d. The rise in AC/TC ratio with time in river water was confirmed in the analysis of two years of Kentucky River water quality data where the average AC/TC ratio during months with high river flow (rain) was 3.37 and rose to an average of 27.58 during months with low flow. The average AC/TC ratio during high flow months compared to that of raw human sewage (3.9) and the ratio increased to values associated with animal impacted urban runoff (18.9) during low flow months.

Download Full-text