scholarly journals Depth Distribution of Added Lead and Zinc in Sand Column of Slow Sand Filter Pond Model.

2001 ◽  
Vol 37 (3) ◽  
pp. 117-122
Author(s):  
MASUO YAMAMOTO ◽  
NOBUTADA NAKAMOTO
Keyword(s):  
Depth Distribution ◽  
Sand Column ◽  
Sand Filter ◽  
Lead And Zinc ◽  
Slow Sand Filter

scholarly journals Vertical Profile of Adhered Suspended Matter and Its Composition in a Surface Sand Column of a Slow Sand Filter.

1996 ◽  
Vol 19 (12) ◽  
pp. 1009-1014
Author(s):  
Noriyasu IWASE ◽  
Yutaka KIZAKI ◽  
Kentaro NOZAKI ◽  
Masashi SAKAI ◽  
Nobutada NAKAMOTO
Keyword(s):  
Suspended Matter ◽  
Vertical Profile ◽  
Sand Column ◽  
Sand Filter ◽  
Slow Sand Filter ◽  
Surface Sand

Inconsiderable Amount of Phytoplankton Leakage by Algal Mat Peeling from a Slow Sand Filter.

1997 ◽  
Vol 33 (4) ◽  
pp. 179-186 ◽  
Author(s):  
NOBUTADA NAKAMOTO ◽  
NORIYASU IWASE ◽  
KENTARO NOZAKI ◽  
MASASHI SAKAI
Keyword(s):  
Sand Filter ◽  
Slow Sand Filter ◽  
Algal Mat

scholarly journals Verification of filter efficiency of horizontal roughing filter by Weglin's design criteria and Artificial Neural Network

2009 ◽  
Vol 2 (1) ◽  
pp. 21-27 ◽  
Author(s):  
◽  
◽  
◽  
◽  
Keyword(s):  
Genetic Model ◽  
Neural Model ◽  
Model Verification ◽  
Design Criteria ◽  
Experimental Setup ◽  
Mean Square ◽  
Raw Water ◽  
Neural Models ◽  
Sand Filter ◽  
Slow Sand Filter

Abstract. The general objective of this study is to estimate the performance of the Horizontal Roughing Filter (HRF) by using Weglin's design criteria based on 1/3–2/3 filter theory. The main objective of the present study is to validate HRF developed in the laboratory with Slow Sand Filter (SSF) as a pretreatment unit with the help of Weglin's design criteria for HRF with respect to raw water condition and neuro-genetic model developed based on the filter dataset. The results achieved from the three different models were compared to find whether the performance of the experimental HRF with SSF output conforms to the other two models which will verify the validity of the former. According to the results, the experimental setup was coherent with the neural model but incoherent with the results from Weglin's formula as lowest mean square error was observed in case of the neuro-genetic model while comparing with the values found from the experimental SSF-HRF unit. As neural models are known to learn a problem with utmost efficiency, the model verification result was taken as positive.

scholarly journals A23 Seasonal abundance of chironomid larvae in slow sand filter bed

2002 ◽  
Vol 53 (Supplement) ◽  
pp. 41
Author(s):  
K. Hirabayashi ◽  
N. Nakamoto ◽  
S. Tanizaki
Keyword(s):  
Seasonal Abundance ◽  
Sand Filter ◽  
Chironomid Larvae ◽  
Slow Sand Filter ◽  
Filter Bed

scholarly journals Influence of slow sand filter cleaning process type on filter media biomass: backwashing versus scraping

2021 ◽  
Vol 189 ◽  
pp. 116581 ◽  
Author(s):  
F.H. de Souza ◽  
P.B. Roecker ◽  
D.D. Silveira ◽  
M.L. Sens ◽  
L.C. Campos
Keyword(s):  
Cleaning Process ◽  
Filter Media ◽  
Sand Filter ◽  
Slow Sand Filter

scholarly journals Streptophyta and Acetic Acid Bacteria Succession Promoted by Brass in Slow Sand Filter System Schmutzdeckes

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Ma. Carmen E. Delgado-Gardea ◽  
Patricia Tamez-Guerra ◽  
Ricardo Gomez-Flores ◽  
Mariela Garfio-Aguirre ◽  
Beatriz A. Rocha-Gutiérrez ◽  
...  
Keyword(s):  
Acetic Acid ◽  
Acetic Acid Bacteria ◽  
Sand Filter ◽  
Filter System ◽  
Slow Sand Filter

scholarly journals Occurrence of Phytophthora Species in Commercial Nursery Production

2016 ◽  
Vol 17 (2) ◽  
pp. 64-75 ◽  
Author(s):  
Corina Junker ◽  
Patrick Goff ◽  
Stefan Wagner ◽  
Sabine Werres
Keyword(s):  
Phytophthora Species ◽  
Phytophthora Ramorum ◽  
Water Recycling ◽  
Infection Rates ◽  
Sand Filter ◽  
Detection Rates ◽  
Water And Sediment ◽  
Nursery Production ◽  
Slow Sand Filter ◽  
Soil Substrates

Two commercial woody ornamental nurseries were sampled for the presence of Phytophthora species over a period of three years between 2011 and 2014. The samples were taken every two months at different propagation (greenhouses, plastic tunnels) and cultivation (container stands) areas as well as from nearby pathways and from a water recycling system with a slow sand filter. Furthermore, different materials like soil, substrates, residues, wind-carried leaves, water and sediment were sampled. In total, 12 known Phytophthora species could be detected. Further, three isolates did not match any of the known species. Phytophthora ramorum, P. gonapodyides, and P. plurivora were the species with the highest detection rates. Phytophthora ramorum could be detected during all seasons of the year. In total, the puddles on the pathways had the highest percentage of positive detections. Residues, wind-carried leaves and water and sediment from the water runoffs were also good places for Phytophthora survival. In both nurseries, the plant samples showed very low infection rates. Ideas for surveys and management are discussed. Accepted for publication 14 March 2016. Published 11 April 2016.

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