scholarly journals Aquatic Vegetation and Invertebrate Communities of Big Stone National Wildlife Refuge

2019 ◽  
Vol 10 (1) ◽  
pp. 277-294
Author(s):  
Brian A. Tangen ◽  
Raymond G. Finocchiaro ◽  
Wesley E. Newton ◽  
Charles F. Dahl

Abstract Observed degradation of aquatic systems at Big Stone National Wildlife Refuge, located in west-central Minnesota, have been associated with sediment-laden inflows from riverine systems. To support management, a study was conducted during 2013–2014 with overall goals of characterizing the aquatic invertebrate and vegetation communities of the Big Stone National Wildlife Refuge and exploring relations between these communities and various water-quality parameters. Sample sites were located along an observed vegetation gradient and assigned to three predetermined habitat zones for comparison purposes: upstream, transition, and downstream. Of the 12 species of aquatic vegetation that were identified, invasive narrowleaf cattail Typha angustifolia dominated the upstream zone (observed at >90% of sample locations), coontail Ceratophyllum demersum and narrowleaf cattail were most common in the transition zone (collected or observed at 100 and 83% of sample locations, respectively), and coontail and narrowleaf pondweed Potamogeton strictifolius were most common in the downstream zone (collected at 100 and 64% of sample locations, respectively). Measured values for the water-quality parameters varied among dates, reflecting the continually fluctuating nature of riverine systems. Based on general observations across sample dates, turbidity and dissolved oxygen concentrations were greatest in the upstream zone sample sites, while oxidation-reduction potential was greatest in the downstream zone sites. There were 115 unique aquatic invertebrate taxa identified to varying levels of taxonomic resolution. Results suggested that there were overall differences in invertebrate biomass among the sample dates, but that there were no strong trends among the sample zones. Aquatic invertebrates and vegetation communities, along with the water-quality parameters, varied temporally and showed irregular relations among the sample zones. These general observations emphasize the importance of temporally and spatially intensive sampling to account for natural variation. Moreover, short- and long-term streamflow and water-level information obtained for this study demonstrated substantial variability that must be considered when conducting biotic inventories and monitoring water quality, as well as when using such data to assess management options. Periodic monitoring of wetlands and associated streamflows, along with sediment loads and water quality of inflows, should allow Big Stone National Wildlife Refuge staff to identify habitat degradation and potential contributing factors, and to develop strategies to achieve specific management objectives and goals.

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.


2020 ◽  
Vol 182 ◽  
pp. 109136
Author(s):  
Oana Mare Roșca ◽  
Thomas Dippong ◽  
Monica Marian ◽  
Cristina Mihali ◽  
Lucia Mihalescu ◽  
...  

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