scholarly journals A Fusion Water Quality Soft-Sensing Method Based on WASP Model and Its Application in Water Eutrophication Evaluation

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Xiaoyi Wang ◽  
Jie Jia ◽  
Tingli Su ◽  
Zhiyao Zhao ◽  
Jiping Xu ◽  
...  
Keyword(s):  
Water Quality ◽  
Unscented Kalman Filter ◽  
Water Environment ◽  
Quality Analysis ◽  
Quality Data ◽  
Model Parameters ◽  
Water Quality Data ◽  
Soft Sensing ◽  
Water Eutrophication ◽  
Quality Indexes

Water environment protection is of great significance for both economic development and improvement of people’s livelihood, where modeling of water environment evolution is indispensable in water quality analysis. However, many water quality indexes related to water quality model cannot be measured online, and some model parameters always vary among different water areas. Thus, this paper proposes a water quality soft-sensing method based on the water quality mechanism model to simulate evolution of water quality indexes online, where unscented Kalman filter is utilized to estimate model parameters. Furthermore, a modified fuzzy comprehensive evaluation method is presented to evaluate the level of water eutrophication condition. Finally, the water quality data collected from Taihu Lake and Beihai Lake are used to validate the effectiveness and generality of the proposed method. The results show that the proposed soft-sensing method is able to describe the variation of related water quality indexes, with better accuracy compared to nonlinear least squares based method and traditional trial-and-error based method. On this basis, the water eutrophication condition can be also accurately evaluated.

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 OPTMIZATION OF BIO-OPTICAL MODEL PARAMETERS FOR TURBID LAKE WATER QUALITY ESTIMATION USING LANDSAT 8 AND WASI-2D

2020 ◽  
Vol XLII-3/W11 ◽  
pp. 67-72
Author(s):  
A. Manuel ◽  
A. C. Blanco ◽  
A. M. Tamondong ◽  
R. Jalbuena ◽  
O. Cabrera ◽  
...  
Keyword(s):  
Water Quality ◽  
Chlorophyll A ◽  
The Philippines ◽  
Quality Data ◽  
Model Parameters ◽  
Landsat 8 ◽  
Water Quality Data ◽  
Chlorophyll A Concentration ◽  
In Situ Data

Abstract. Laguna Lake, the Philippines’ largest freshwater lake, has always been historically, economically, and ecologically significant to the people living near it. However, as it lies at the center of urban development in Metro Manila, it suffers from water quality degradation. Water quality sampling by current field methods is not enough to assess the spatial and temporal variations of water quality in the lake. Regular water quality monitoring is advised, and remote sensing addresses the need for a synchronized and frequent observation and provides an efficient way to obtain bio-optical water quality parameters. Optimization of bio-optical models is done as local parameters change regionally and seasonally, thus requiring calibration. Field spectral measurements and in-situ water quality data taken during simultaneous satellite overpass were used to calibrate the bio-optical modelling tool WASI-2D to get estimates of chlorophyll-a concentration from the corresponding Landsat-8 images. The initial output values for chlorophyll-a concentration, which ranges from 10–40 μg/L, has an RMSE of up to 10 μg/L when compared with in situ data. Further refinements in the initial and constant parameters of the model resulted in an improved chlorophyll-a concentration retrieval from the Landsat-8 images. The outputs provided a chlorophyll-a concentration range from 5–12 μg/L, well within the usual range of measured values in the lake, with an RMSE of 2.28 μg/L compared to in situ data.

Forecasting Model Based on SAGA and LS-SVM for the Water Quality of Poyang Lake

2011 ◽  
Vol 347-353 ◽  
pp. 781-785
Author(s):  
Qun Cao ◽  
Bing Xiang Liu ◽  
Xiang Chen
Keyword(s):  
Water Quality ◽  
Poyang Lake ◽  
Quality Data ◽  
Support Vector ◽  
Forecasting Model ◽  
Model Parameters ◽  
Water Quality Data ◽  
Model Based ◽  
Simulated Annealing Genetic Algorithm

According to the nonlinearity and uncertainty of the water quality data samples, a forecasting model based on Simulated Annealing Genetic Algorithm(SAGA)and least squares support vector machines(LS-SVM) is proposed. Through adaptively optimizing the model parameters of LS-SVM by SAGA, we can apply the model to forecast water quality of Poyang Lake. The experimental results indicate that compared to the typical LS-SVM,the model is very practical and with higher precision.

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 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,.

scholarly journals Characterizing water quality monitoring visualization with Hadoop and Google Maps

2017 ◽  
Vol 12 (4) ◽  
pp. 882-893 ◽  
Author(s):  
Weijian Huang ◽  
Xinfei Zhao ◽  
Yuanbin Han ◽  
Wei Du ◽  
Yao Cheng
Keyword(s):  
Water Quality ◽  
Water Environment ◽  
Large Data ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Quality Data ◽  
Data Sets ◽  
Google Maps ◽  
Water Quality Data ◽  
Computer Clusters

Abstract In water quality monitoring, the complexity and abstraction of water environment data make it difficult for staff to monitor the data efficiently and intuitively. Visualization of water quality data is an important part of the monitoring and analysis of water quality. Because water quality data have geographic features, their visualization can be realized using maps, which not only provide intuitive visualization, but also reflect the relationship between water quality and geographical position. For this study, the heat map provided by Google Maps was used for water quality data visualization. However, as the amount of data increases, the computational efficiency of traditional development models cannot meet the computing task needs quickly. Effective storage, extraction and analysis of large water data sets becomes a problem that needs urgent solution. Hadoop is an open source software framework running on computer clusters that can store and process large data sets efficiently, and it was used in this study to store and process water quality data. Through reasonable analysis and experiment, an efficient and convenient information platform can be provided for water quality monitoring.

scholarly journals On the value of water quality data and informative flow states in karst modelling

2017 ◽  
Vol 21 (12) ◽  
pp. 5971-5985 ◽  
Author(s):  
Andreas Hartmann ◽  
Juan Antonio Barberá ◽  
Bartolomé Andreo
Keyword(s):  
Water Quality ◽  
Water Resource ◽  
Quality Data ◽  
Model Parameters ◽  
Flow State ◽  
Saturated Flow ◽  
Water Quality Data ◽  
Model Parameter ◽  
Time Period ◽  
Flow States

Abstract. If properly applied, karst hydrological models are a valuable tool for karst water resource management. If they are able to reproduce the relevant flow and storage processes of a karst system, they can be used for prediction of water resource availability when climate or land use are expected to change. A common challenge to apply karst simulation models is the limited availability of observations to identify their model parameters. In this study, we quantify the value of information when water quality data (NO3− and SO42−) is used in addition to discharge observations to estimate the parameters of a process-based karst simulation model at a test site in southern Spain. We use a three-step procedure to (1) confine an initial sample of 500 000 model parameter sets by discharge and water quality observations, (2) identify alterations of model parameter distributions through the confinement, and (3) quantify the strength of the confinement for the model parameters. We repeat this procedure for flow states, for which the system discharge is controlled by the unsaturated zone, the saturated zone, and the entire time period including times when the spring is influenced by a nearby river. Our results indicate that NO3− provides the most information to identify the model parameters controlling soil and epikarst dynamics during the unsaturated flow state. During the saturated flow state, SO42− and discharge observations provide the best information to identify the model parameters related to groundwater processes. We found reduced parameter identifiability when the entire time period is used as the river influence disturbs parameter estimation. We finally show that most reliable simulations are obtained when a combination of discharge and water quality date is used for the combined unsaturated and saturated flow states.

Calculation and Analysis of the Water Environment Capacity for the Yuanhe River in Jiangxi Province, China

2011 ◽  
Vol 281 ◽  
pp. 137-140 ◽  
Author(s):  
Mao Lan Wang ◽  
Bin Luo ◽  
Wen Bin Zhou
Keyword(s):  
Water Pollution ◽  
Water Quality ◽  
Water Environment ◽  
Quality Data ◽  
Jiangxi Province ◽  
Residual Water ◽  
Water Quality Data ◽  
Control Units ◽  
Environment Capacity ◽  
Water Environment Capacity

Yuanhe River is a major source of drinking, irrigation, industrial, hydropower generation, and recreational water for the circumjacent city. It has more serious water pollution problems because it flows through some heavy industry cities. So basis of the river water environment functional zones combined the various water quality data and the monitoring hydrological data, the water environment capacity of the Yuanhe River was calculated by using the one-dimensional water quality model. The results show that the water environment capacity is 112650 t/yr for chemical oxygen demand (COD) and 3265 t/yr for ammonium nitrogen (NH3-N). Most of the control units have residual water environment capacity, only individual control units have the serious water pollution and its residual capacity of COD and NH3-N is below 0, so it is necessary to strengthen the pollution control of these control units.

Widespread global concern over harm from drinking water

2021 ◽  
Author(s):  
Sera Young ◽  
Joshua Miller ◽  
Chad Staddon ◽  
Aaron Salzberg ◽  
Julius Lucks ◽  
...  
Keyword(s):  
Water Quality ◽  
Drinking Water ◽  
Risk Perceptions ◽  
Water Environment ◽  
Quality Data ◽  
Safe Drinking Water ◽  
Future Expectations ◽  
Water Quality Data ◽  
Current Water ◽  
Nationally Representative

Abstract Poor drinking water quality is a global crisis that affects billions of individuals. Understanding who is most impacted is necessary to develop programs that ensure sustainable, reliable, and resilient access to safe water. But current water indicators do not capture people’s experienced and anticipated harm from drinking water, which means we have had limited understanding of how individuals conceptualize, navigate, and are affected by their water environment. Here, we analyzed data from nationally representative surveys undertaken in 142 countries in which people reported their recent experiences and future expectations of harm from drinking water. Prevalence of reported harm from drinking water in the prior two years was 14.5% (range: 0.8%–54.3%). More than half of the world’s population (54.4%) anticipated that they would experience serious harm from their drinking water in the next two years. Greater public sector corruption was associated with greater anticipated harm from drinking water, even when adjusting for indicators of water infrastructure and economic development. Disparities in anticipated harm across countries and by gender and household location indicate that targeted policies are required to address risk perceptions, equitably improve access to safe drinking water, and increase trust in institutions that supply and regulate water services. The addition of experiential survey data to global data collection efforts will complement objective water quality data and provide novel insights about which strategies will most effectively advance progress toward safe drinking water for all.

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