scholarly journals GRQA: Global River Water Quality Archive

2021 ◽  
Author(s):  
Holger Virro ◽  
Giuseppe Amatulli ◽  
Alexander Kmoch ◽  
Longzhu Shen ◽  
Evelyn Uuemaa
Keyword(s):  
Water Quality ◽  
Time Series ◽  
Quality Measurement ◽  
Observation Time ◽  
Quality Analysis ◽  
Quality Data ◽  
Observation Data ◽  
Water Quality Data ◽  
Spatial Coverage ◽  
Global Datasets

Abstract. A major problem related to global water quality analysis and modelling has been the lack of available good quality and consistent water quality measurement datasets with a global spatial coverage. Current study aims to contribute into improving the global datasets on water quality by aggregating and harmonizing five national, continental and global datasets: CESI, GEMSTAT, GLORICH, WATERBASE and WQP. 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 16 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 repository of the University of Tartu, DataDOI, http://dx.doi.org/10.23673/re-273 (Virro et al., 2021).

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 Evaluation of statistical methods for quantifying fractal scaling in water-quality time series with irregular sampling

2018 ◽  
Vol 22 (2) ◽  
pp. 1175-1192 ◽  
Author(s):  
Qian Zhang ◽  
Ciaran J. Harman ◽  
James W. Kirchner
Keyword(s):  
Water Quality ◽  
Time Series ◽  
Irregular Sampling ◽  
Quality Data ◽  
Sampled Data ◽  
Spectral Slope ◽  
Water Quality Data ◽  
Fractal Scaling ◽  
Wide Range ◽  
Irregularly Sampled Data

Abstract. River water-quality time series often exhibit fractal scaling, which here refers to autocorrelation that decays as a power law over some range of scales. Fractal scaling presents challenges to the identification of deterministic trends because (1) fractal scaling has the potential to lead to false inference about the statistical significance of trends and (2) the abundance of irregularly spaced data in water-quality monitoring networks complicates efforts to quantify fractal scaling. Traditional methods for estimating fractal scaling – in the form of spectral slope (β) or other equivalent scaling parameters (e.g., Hurst exponent) – are generally inapplicable to irregularly sampled data. Here we consider two types of estimation approaches for irregularly sampled data and evaluate their performance using synthetic time series. These time series were generated such that (1) they exhibit a wide range of prescribed fractal scaling behaviors, ranging from white noise (β  =  0) to Brown noise (β  =  2) and (2) their sampling gap intervals mimic the sampling irregularity (as quantified by both the skewness and mean of gap-interval lengths) in real water-quality data. The results suggest that none of the existing methods fully account for the effects of sampling irregularity on β estimation. First, the results illustrate the danger of using interpolation for gap filling when examining autocorrelation, as the interpolation methods consistently underestimate or overestimate β under a wide range of prescribed β values and gap distributions. Second, the widely used Lomb–Scargle spectral method also consistently underestimates β. A previously published modified form, using only the lowest 5 % of the frequencies for spectral slope estimation, has very poor precision, although the overall bias is small. Third, a recent wavelet-based method, coupled with an aliasing filter, generally has the smallest bias and root-mean-squared error among all methods for a wide range of prescribed β values and gap distributions. The aliasing method, however, does not itself account for sampling irregularity, and this introduces some bias in the result. Nonetheless, the wavelet method is recommended for estimating β in irregular time series until improved methods are developed. Finally, all methods' performances depend strongly on the sampling irregularity, highlighting that the accuracy and precision of each method are data specific. Accurately quantifying the strength of fractal scaling in irregular water-quality time series remains an unresolved challenge for the hydrologic community and for other disciplines that must grapple with irregular sampling.

scholarly journals Dampak Limbah Penambangan Emas Tanpa Izin (Peti) Terhadap Kualitas Air Sungai Limun Kabupaten Sarolangun Propinsi Jambi - The Impact Of Illegal Gold Mining Activity To The Water Quality Of Limun River, Sarolangun District, Jambi Province

2017 ◽  
Vol 14 (3) ◽  
pp. 251
Author(s):  
Rita Yulianti ◽  
Emi Sukiyah ◽  
Nana Sulaksana
Keyword(s):  
Water Quality ◽  
Gold Mining ◽  
Quality Standard ◽  
Water Levels ◽  
Quality Analysis ◽  
Quality Data ◽  
Mining Activities ◽  
Water Quality Data ◽  
The Impact

Daerah penelitian terletak di desa Muaro Limun, Kecamatan Limun Kabupaten Sarolangun Provinsi Jambi. Sungai limun, salah satu sungai besar di daerah kabupaten sarolangun yang dimanfaatkan oleh mayarakat sekitarnya sebagai sumber penghidupan. Penelitian bertujuan untuk mengetahui pengaruh kegiatan penambangan terhadap kualitas air sungai Batang Limun, dan perubahan sifat fisik dan  kimia yang diakibatkan   kegiatan penambangan.Metode yang digunakan adalah  metode grab sampel, serta stream sedimen untuk dianalis di laboratorium. Sejumlah sampel diambil di beberapa lokasi Penambangan Emas berdasarkan Aliran Sub-DAS dan dibandingkan dengan beberapa sampel lain yang diambil pada lokasi yang belum terkontaminasi oleh kegiatan penambangan. Analisis kualitas air mengacu pada  SMEWWke 22 tahun 2012 dan standar baku mutu air kelas II dalam PP No 82 yang dikeluarkan oleh Menteri Kesehatan No. 492/Menkes/Per/IV/2010. Diketahui sungai Batang Limun telah mengalami perubahan karakteristik fisika dan kimia. Dari grafik  kosentrasi kekeruhan, pH, TSS, TDS  Cu, Pb, Zn, Mn, Hg terlihat bahwa penambang emas tanpa izin (PETI) dengan cara amalgamasi yang menyebabkan terjadinya penurunan kualitas air sungai. Sejak tahun 2009 sampai tahun 2015  sungai Limun dan sekitarnya terus mengalami penurunan kualitas air. Penurunan kualitas yang cukup tinggi terjadi  yaitu peningkatan nilai Rata-rata konsentrasi merkuri pada sungai Batang Limun dari 0,18ppb (0,00018 mg/l)  menjadi 0,3ppb (0,0003 mg/l), peningkatan tersebut dipengaruhi oleh proses kegiatan penambangan dan nilai tersebut masih dibawah standar baku mutu air kelas II  pp nomor 82 tahun 2010.Kata kunci :   Kualitas Air, Sungai Limun,TSS, Merkuri, PETI Limun river is one of the major rivers in the area of Sarolangun, which utilized by the society as a source of livelihood. The aim of study  to analyze the effect of mining activities on  the water quality of Batang Limun River, and the changes of physical and chemical properties of water. The method used are grab  and stream samples to  sediment analyzed in the laboratory. A number of samples were taken at several locations based Flow Gold Mining Sub-watershed and compared to some other samples taken at the location that has not been contaminated by mining activities. Water quality analysis referring to SMEWW, 22nd edition 2012 and refers to Regulation No 82 that issued by Minister of Health No. 492 / Menkes / Per / IV / 2010.The results showed that the Limun river has undergone chemical changes in physical characteristics. These symptoms can be seen from the discoloration of clear water in the river before the mine becomes brownish after mining, based on graphic of muddiness concentration: pH, TSS, TDS Cu, Pb, Zn, Mn, Hg have seen that  the illegal miner which used amalgamation caused deterioration in water quality, data from 2009 to 2015 Limun river and surrounding areas continue to experience a decrease in water quality. The decreasing of water quality showed in the TSS parameter which found in the area is to high based on  the standard of water quality class II pp number 82 of 2010. An increase in the value of average concentrations of mercury in the Batang Limun river before mine 0,18ppb (0.00018 mg / l) into 0,3ppb (0.0003 mg / l) on the river after the mine. The increase was affected by the mining activities and the value is still below the air quality standard Grade II pp numbers 82 years 2010, although the value is still below with the standards quality standard, the mercury levels in water should still be a major concern because if it accumulates continuously in the water levels will increase and will be bad for health. In contrast to the concentration of mercury in sediments that have a higher value is 153 ppb (0,513ppm ) .Key Words :   Water Quality, Limun River, Mercury, Illegal gold mining

scholarly journals An Observational Process Ontology-Based Modeling Approach for Water Quality Monitoring

Water ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 715
Author(s):  
Xiaolei Wang ◽  
Haitao Wei ◽  
Nengcheng Chen ◽  
Xiaohui He ◽  
Zhihui Tian
Keyword(s):  
Water Quality ◽  
Water Quality Monitoring ◽  
Process Models ◽  
Quality Monitoring ◽  
Quality Data ◽  
Observation Data ◽  
Process Ontology ◽  
Water Quality Data ◽  
Data Process ◽  
Monitoring Process

The increasing deterioration of aquatic environments has attracted more attention to water quality monitoring techniques, with most researchers focusing on the acquisition and assessment of water quality data, but seldom on the discovery and tracing of pollution sources. In this study, a semantic-enhanced modeling method for ontology modeling and rules building is proposed, which can be used for river water quality monitoring and relevant data observation processing. The observational process ontology (OPO) method can describe the semantic properties of water resources and observation data. In addition, it can provide the semantic relevance among the different concepts involved in the observational process of water quality monitoring. A pollution alert can be achieved using the reasoning rules for the water quality monitoring stations. In this study, a case is made for the usability testing of the OPO models and reasoning rules by utilizing a water quality monitoring system. The system contributes to the water quality observational monitoring process and traces the source of pollutants using sensors, observation data, process models, and observation products that users can access in a timely manner.

scholarly journals Time series modeling of biochemical oxygen demand at the upstream catchment of Feitsui Reservoir, Taiwan

2016 ◽  
Vol 47 (5) ◽  
pp. 1069-1085 ◽  
Author(s):  
Yung-Chia Chiu ◽  
Chih-Wei Chiang ◽  
Tsung-Yu Lee
Keyword(s):  
Water Quality ◽  
Time Series ◽  
Biochemical Oxygen Demand ◽  
Oxygen Demand ◽  
Quality Data ◽  
Series Data ◽  
Water Quality Data ◽  
Inference System ◽  
Feitsui Reservoir ◽  
Upstream Catchment

The adaptive neuro fuzzy inference system (ANFIS) has been proposed to model the time series of water quality data in this study. The biochemical oxygen demand data collected at the upstream catchment of Feitsui Reservoir in Taiwan for more than 20 years are selected as the target water quality variable. The classical statistical technique of the Box-Jenkins method is applied for the selection of appropriate input variables and data pre-processing of using differencing is implemented during the model development. The time series data obtained by ANFIS models are compared to those obtained by autoregressive integrated moving average (ARIMA) and artificial neural networks (ANNs). The results show that the ANFIS model identified at each sampling station is superior to the respective ARIMA and ANN models. The R values at all sampling stations of the training and testing datasets are 0.83–0.98 and 0.81–0.89, respectively, except at Huang-ju-pi-liao station. ANFIS models can provide accurate predictions for complex hydrological processes, and can be extended to other areas to improve the understanding of river pollution trends. The procedure of input selection and the pre-processing of input data proposed in this study can stimulate the usage of ANFIS in other related studies.

scholarly journals ANALISIS KONDISI LAMUN (SEAGRASS) DI PERAIRAN PULAU PRAMUKA, KEPULAUAN SERIBU

2015 ◽  
Vol 13 (1) ◽  
pp. 22-32
Author(s):  
Septi Dwi Fajarwati ◽  
Asma Irma Setianingsih ◽  
Muzani Muzani
Keyword(s):  
Water Quality ◽  
Community Structure ◽  
Ph Value ◽  
Secondary Data ◽  
Quality Analysis ◽  
Quality Data ◽  
Primary Data ◽  
Seagrass Habitat ◽  
Percentage Cover ◽  
Water Quality Data

ABSTRACT This research aims to analyze the condition of seagrass ecosystem to see water quality data of the seagrass habitat and percentage cover of seagrass in the waters of the Pramuka Island, Seribu Islands. The research was conducted over two months from October to November 2014.This research used a descriptive method with field survey approach. The population in this study is the seagrass in Waters Pramuka Island. Determining the location with purposive sampling of the sampling is divided into three stations is North, East and South. Data collection techniques include primary data and secondary data. Primary data is data of seagrass (type, percentage cover and density of seagrass) and data of seagrass habitat environmental parameters (water temperature, current speed, brightness, depth, salinity, substrate, TSS, DO, pH) were obtained by direct measurement in the field, while secondary data include the general state of the research sites. Data analysis techniques used in this study using analysis of community structure of seagrass and water quality analysis. The results showed that seagrass species found in the Pramuka Island there are 6 types of seagrass Cymodocea rotundata, Cymodocea serrulata, Enhalus acoroides, Halophila ovalis, Halodule uninervis, Thalassia hemprichii. Conditions of seagrass in the waters of the Pramuka Island included into the category of less healthy-poor seagrass. At station 1 percentage by 31% classified seagrass less healthy conditions, while the other two stations are stations 2 and 3 belong to the category of the poor condition of seagrass, with each percentage cover of seagrass 19.4% and 20.3%. Of all water quality parameters measured, all the parameters are still in normal circumstances, but there are some parameters whose value is high at some stations TSS and pH value is high at station 2 with a value of TSS 18 mg/l and a pH value of 8.2. Water quality and seagrass communities in station 1 is still in good condition for the growth of seagrass, because at this station is an unspoiled area away from human activity, while the stations 2 and 3 have undergone changes in community structure of seagrass because at this station has several anthropogenic activities that disrupt the lives of seagrass, mostly from human activities such as domestic sewage and hoarding/reclamation, which affects the condition of seagrass at station 2 and 3 are poor seagrass. Keyword: Seagrass, Water Quality, Pramuka Island

scholarly journals PERENCANAAN ARTIFICIAL REEF SEBAGAI RESTORASI TERUMBU KARANG DAN PENGAMAN PANTAIDI PULAU LEMUKUTAN KABUPATEN BENGKAYANG

2015 ◽  
Vol 3 (1) ◽  
Author(s):  
Dhiecho Mahar Dhiecha
Keyword(s):  
Water Quality ◽  
Coral Reefs ◽  
Life Quality ◽  
Artificial Reef ◽  
Thick Layer ◽  
Secondary Data ◽  
Quality Analysis ◽  
Quality Data ◽  
Coastal Protection ◽  
Water Quality Data

ABSTRACT Damage that occurs around the area Lemukutan Island caused the use of chemicals or cyanide to catch fish and coral reefs by local people, but it is also often made use of bombs surrounding communities to take beautiful corals that will be sold to destroy coral reef ecosystems in the waters .Artificial reef planning methods (Artificial reef ) as the restoration of coral reefs and coastal protection is to conduct a field survey using a measuring instrument GPS topographic, marine water quality data and using secondary data, statistical data, tidal, wave height, bathymetry map, direction of flow and wind direction. Water quality analysis carried out in-situ, parameter test in the brightness of the water, currents, salinity, temperature, pH. Analysis of the function of Artificial reefs for reef restoration and as coastal protection is to use a hollow dome type or reef balls. Appropriate placement location and located at coordinates N 00 45 '33.8 ", E 1080 42' 19.5" up to N 00 45 '29.2 "E 1070 15' 49.0", and the average depth of 3 meters. Results of water quality testing based on parameters salinity, current velocity, pH, turbidity, light intensity and temperature qualify coral life quality standards in Indonesia based on PERMEN LH No. 51 TAHUN 2004. The dimensions of Artificial reef s diameter of 1.80 m, height 1.50 m with a thick layer of 10 cm and a hole located on the sides of the Artificial reef for 34 holes with a diameter of 15 cm. Filler material used is concrete with a volume of 0.916 m3, equivalent to 2,198 tons. Binder or cement used type V, which is resistant to high sulfate levels. The amount of reef balls used is 834 pieces. Keywords: Artificial reef , Seawater Quality, Reef balls and coral reefs,.

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