scholarly journals Water Quality Parameters: Statistical Point of View

2020 ◽  
1970 ◽  
Vol 45 (4) ◽  
pp. 289-302 ◽  
Author(s):  
Ali S Amiri ◽  
E Morsheda ◽  
AI Kazi ◽  
S Siraj ◽  
S Ahmed ◽  
...  

The Bangladesh Environmental Technology Verification - Support to Arsenic Mitigation (BETV-SAM) program objectively assessed thirteen arsenic removal technologies (ARTs) that had been submitted for performance verification, and selected seven technologies for the field testing and performance verification. Each technology was deployed to five hydrogeologically distinct regions of Bangladesh and was tested on five wells (with replicate units installed and tested on two wells). Raw and treated water samples were collected at regular intervals and were analysed for arsenic and other water quality parameters. The data were analysed using standard statistical software and the performance of individual ARTs have been evaluated and compared with the proponent's claim. Two technologies (STAR & CIWP) performed well while five other technologies performed poorly and their claims were rejected. The results of the first-round of the BETV-SAM field testing and performance verification program presented and discussed in this paper, may be important from a technological design and development point of view. Key words: Arsenic removal technology; Field testing; Performance verification; Water quality parameters. DOI: 10.3329/bjsir.v45i4.7325 Bangladesh J. Sci. Ind. Res. 45(4), 289-302, 2010


2015 ◽  
Vol 8 (1) ◽  
pp. 85-89
Author(s):  
F Zannat ◽  
MA Ali ◽  
MA Sattar

A study was conducted to evaluate the water quality parameters of pond water at Mymensingh Urban region. The water samples were collected from 30 ponds located at Mymensingh Urban Region during August to October 2010. The chemical analyses of water samples included pH, EC, Na, K, Ca, S, Mn and As were done by standard methods. The chemical properties in pond water were found pH 6.68 to 7.14, EC 227 to 700 ?Scm-1, Na 15.57 to 36.00 ppm, K 3.83 to 16.16 ppm, Ca 2.01 to 7.29 ppm, S 1.61 to 4.67 ppm, Mn 0.33 to 0.684 ppm and As 0.0011 to 0.0059 ppm. The pH values of water samples revealed that water samples were acidic to slightly alkaline in nature. The EC value revealed that water samples were medium salinity except one sample and also good for irrigation. According to drinking water standard Mn toxicity was detected in pond water. Considering Na, Ca and S ions pond water was safe for irrigation and aquaculture. In case of K ion, all the samples were suitable for irrigation but unsuitable for aquaculture.J. Environ. Sci. & Natural Resources, 8(1): 85-89 2015


2018 ◽  
Vol 69 (8) ◽  
pp. 2045-2049
Author(s):  
Catalina Gabriela Gheorghe ◽  
Andreea Bondarev ◽  
Ion Onutu

Monitoring of environmental factors allows the achievement of some important objectives regarding water quality, forecasting, warning and intervention. The aim of this paper is to investigate water quality parameters in some potential pollutant sources from northern, southern and east-southern areas of Romania. Surface water quality data for some selected chemical parameters were collected and analyzed at different points from March to May 2017.


1982 ◽  
Vol 14 (4-5) ◽  
pp. 43-58 ◽  
Author(s):  
M Rizet ◽  
J Mouchet

This study was conducted in order to understand the taste and odour problems that occurred in the Seine and the Marne rivers during the severe drought of 1976. Samples were taken every 15 days from several locations in the rivers themselves and from storage reservoirs upstream from Paris. Algae and actinomycetes were identified and counted. Metabolite concentrations were measured. These data were correlated with threshold odor numbers and bacteriological water quality parameters.


Water ◽  
2016 ◽  
Vol 8 (11) ◽  
pp. 507 ◽  
Author(s):  
Iván Vizcaíno ◽  
Enrique Carrera ◽  
Margarita Sanromán-Junquera ◽  
Sergio Muñoz-Romero ◽  
José Luis Rojo-Álvarez ◽  
...  

2021 ◽  
Vol 9 (5) ◽  
pp. 474
Author(s):  
René Rodríguez-Grimón ◽  
Nestor Hernando Campos ◽  
Ítalo Braga Castro

Since 2013, there has been an increase (>23%) in naval traffic using maritime routes and ports on the coastal fringe of Santa Marta, Colombia. Of major concern, and described by several studies, is the relationship between maritime traffic and coastal contamination. This study proposed a maritime traffic indicator considering the simultaneous effects of several relevant measurements of water quality parameters to estimate the impact of naval activity. The approach involved developing a model including the number of vessels, hull length, and permanence time in berths. In addition, water quality variables, considering climatic seasons, were used to verify association with maritime traffic and touristic activities. The high concentrations of total coliforms (TC) and dissolved/dispersed petroleum hydrocarbons in chrysene equivalents (DDPH) reported by the International Marina of Santa Marta (SM) were affected by the local anthropic activities, including tourism, naval traffic, and urban wastewater discharges. Moreover, our results suggest the occurrence of multiple chemical impacts within Tayrona National Natural Park (PNNT) affecting conservation goals. The estimation of the maritime traffic indicator proposed in this study may be an easy and more complete tool for future studies evaluating the impact of naval activities on environmental quality.


Water ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 1547
Author(s):  
Jian Sha ◽  
Xue Li ◽  
Man Zhang ◽  
Zhong-Liang Wang

Accurate real-time water quality prediction is of great significance for local environmental managers to deal with upcoming events and emergencies to develop best management practices. In this study, the performances in real-time water quality forecasting based on different deep learning (DL) models with different input data pre-processing methods were compared. There were three popular DL models concerned, including the convolutional neural network (CNN), long short-term memory neural network (LSTM), and hybrid CNN–LSTM. Two types of input data were applied, including the original one-dimensional time series and the two-dimensional grey image based on the complete ensemble empirical mode decomposition algorithm with adaptive noise (CEEMDAN) decomposition. Each type of input data was used in each DL model to forecast the real-time monitoring water quality parameters of dissolved oxygen (DO) and total nitrogen (TN). The results showed that (1) the performances of CNN–LSTM were superior to the standalone model CNN and LSTM; (2) the models used CEEMDAN-based input data performed much better than the models used the original input data, while the improvements for non-periodic parameter TN were much greater than that for periodic parameter DO; and (3) the model accuracies gradually decreased with the increase of prediction steps, while the original input data decayed faster than the CEEMDAN-based input data and the non-periodic parameter TN decayed faster than the periodic parameter DO. Overall, the input data preprocessed by the CEEMDAN method could effectively improve the forecasting performances of deep learning models, and this improvement was especially significant for non-periodic parameters of TN.


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