scholarly journals Legionella Diversity and Spatiotemporal Variation in the Occurrence of Opportunistic Pathogens within a Large Building Water System

Pathogens ◽  
2020 ◽  
Vol 9 (7) ◽  
pp. 567 ◽  
Author(s):  
Helen Y. Buse ◽  
Brian J. Morris ◽  
Vicente Gomez-Alvarez ◽  
Jeffrey G. Szabo ◽  
John S. Hall
Keyword(s):  
Water Quality ◽  
Water System ◽  
Bulk Water ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Opportunistic Pathogens ◽  
Water Age ◽  
Large Building ◽  
Survival Mechanisms ◽  
Culture Positive

Understanding Legionella survival mechanisms within building water systems (BWSs) is challenging due to varying engineering, operational, and water quality characteristics unique to each system. This study aimed to evaluate Legionella, mycobacteria, and free-living amoebae occurrence within a BWS over 18–28 months at six locations differing in plumbing material and potable water age, quality, and usage. A total of 114 bulk water and 57 biofilm samples were analyzed. Legionella culturability fluctuated seasonally with most culture-positive samples being collected during the winter compared to the spring, summer, and fall months. Positive and negative correlations between Legionella and L. pneumophila occurrence and other physiochemical and microbial water quality parameters varied between location and sample types. Whole genome sequencing of 19 presumptive Legionella isolates, from four locations across three time points, identified nine isolates as L. pneumophila serogroup (sg) 1 sequence-type (ST) 1; three as L. pneumophila sg5 ST1950 and ST2037; six as L. feeleii; and one as Ochrobactrum. Results showed the presence of a diverse Legionella population with consistent and sporadic occurrence at four and two locations, respectively. Viewed collectively with similar studies, this information will enable a better understanding of the engineering, operational, and water quality parameters supporting Legionella growth within BWSs.

Hazardous events in membrane bioreactors – Part 1: Impacts on key operational and bulk water quality parameters

2016 ◽  
Vol 497 ◽  
pp. 494-503 ◽  
Author(s):  
Trang Trinh ◽  
Amos Branch ◽  
Adam C. Hambly ◽  
Guido Carvajal ◽  
Heather M. Coleman ◽  
...  
Keyword(s):  
Water Quality ◽  
Bulk Water ◽  
Quality Parameters ◽  
Membrane Bioreactors ◽  
Water Quality Parameters

scholarly journals In-Situ Water Quality Observations under a Large-Scale Floating Solar Farm Using Sensors and Underwater Drones

2021 ◽  
Vol 13 (11) ◽  
pp. 6421
Author(s):  
Rui L. Pedroso de Lima ◽  
Katerina Paxinou ◽  
Floris C. Boogaard ◽  
Olof Akkerman ◽  
Fen-Yu Lin
Keyword(s):  
Water Quality ◽  
Large Scale ◽  
Water System ◽  
Open Water ◽  
Quality Parameters ◽  
Climatic Conditions ◽  
Water Quality Parameters ◽  
Solar Panels ◽  
Floating Structure

The rapid implementation of large scale floating solar panels has consequences to water quality and local ecosystems. Environmental impacts depend on the dimensions, design and proportions of the system in relation to the size of the surface water, as well as the characteristics of the water system (currents, tidal effects) and climatic conditions. There is often no time (and budget) for thorough research into these effects on ecology and water quality. A few studies have addressed the potential impacts of floating solar panels, but often rely on models without validation with in situ data. In this work, water quality sensors continuously monitored key water quality parameters at two different locations: (i) underneath a floating solar park; (ii) at a reference location positioned in open water. An underwater drone was used to obtain vertical profiles of water quality and to collect underwater images. The results showed little differences in the measured key water quality parameters below the solar panels. The temperature at the upper layers of water was lower under the solar panels, and there were less detected temperature fluctuations. A biofouling layer on the floating structure was visible in the underwater images a few months after the construction of the park.

Investigation of Some Water Quality Parameters of Pond Water under Mymensingh Municipality

2015 ◽  
Vol 8 (1) ◽  
pp. 85-89
Author(s):  
F Zannat ◽  
MA Ali ◽  
MA Sattar
Keyword(s):  
Water Quality ◽  
Water Samples ◽  
Chemical Properties ◽  
Pond Water ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Urban Region ◽  
Mn Toxicity ◽  
Ph Values ◽  
Drinking Water Standard

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

Assessement of Water Quality Parameters in Some Potential Pollutant Areas from Romania

2018 ◽  
Vol 69 (8) ◽  
pp. 2045-2049
Author(s):  
Catalina Gabriela Gheorghe ◽  
Andreea Bondarev ◽  
Ion Onutu
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
Surface Water Quality ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Quality Data ◽  
Chemical Parameters ◽  
Water Quality Data ◽  
Pollutant Sources ◽  
Water Quality Forecasting

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.

Influence of Discharges from Storage Reservoirs on the Tastes and Odours Appearing in the Seine and Marne Rivers

1982 ◽  
Vol 14 (4-5) ◽  
pp. 43-58 ◽  
Author(s):  
M Rizet ◽  
J Mouchet
Keyword(s):  
Water Quality ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Severe Drought ◽  
Storage Reservoirs ◽  
Metabolite Concentrations

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.

scholarly journals Spatio-Temporal Analysis of Water Quality Parameters in Machángara River with Nonuniform Interpolation Methods

Water ◽  
2016 ◽  
Vol 8 (11) ◽  
pp. 507 ◽  
Author(s):  
Iván Vizcaíno ◽  
Enrique Carrera ◽  
Margarita Sanromán-Junquera ◽  
Sergio Muñoz-Romero ◽  
José Luis Rojo-Álvarez ◽  
...  
Keyword(s):  
Water Quality ◽  
Temporal Analysis ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Interpolation Methods ◽  
Spatio Temporal

scholarly journals Effect of Maritime Traffic on Water Quality Parameters in Santa Marta, Colombia

2021 ◽  
Vol 9 (5) ◽  
pp. 474
Author(s):  
René Rodríguez-Grimón ◽  
Nestor Hernando Campos ◽  
Ítalo Braga Castro
Keyword(s):  
Water Quality ◽  
Petroleum Hydrocarbons ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Maritime Traffic ◽  
Natural Park ◽  
High Concentrations ◽  
The Impact ◽  
The Relationship ◽  
Multiple Chemical

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.

scholarly journals Comparison of Forecasting Models for Real-Time Monitoring of Water Quality Parameters Based on Hybrid Deep Learning Neural Networks

Water ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 1547
Author(s):  
Jian Sha ◽  
Xue Li ◽  
Man Zhang ◽  
Zhong-Liang Wang
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Deep Learning ◽  
Real Time ◽  
Best Management Practices ◽  
Input Data ◽  
Management Practices ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Real Time Monitoring

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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