↓ Skip to main content

Replicating the microbial community and water quality performance of full-scale slow sand filters in laboratory-scale filters

Overview of attention for article published in Water Research, May 2014
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (79th percentile)
  • High Attention Score compared to outputs of the same age and source (91st percentile)

Mentioned by

twitter
9 X users

Citations

dimensions_citation
45 Dimensions

Readers on

mendeley
142 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Replicating the microbial community and water quality performance of full-scale slow sand filters in laboratory-scale filters
Published in
Water Research, May 2014
DOI 10.1016/j.watres.2014.05.008
Pubmed ID
Authors

Sarah-Jane Haig, Christopher Quince, Robert L. Davies, Caetano C. Dorea, Gavin Collins

Abstract

Previous laboratory-scale studies to characterise the functional microbial ecology of slow sand filters have suffered from methodological limitations that could compromise their relevance to full-scale systems. Therefore, to ascertain if laboratory-scale slow sand filters (L-SSFs) can replicate the microbial community and water quality production of industrially operated full-scale slow sand filters (I-SSFs), eight cylindrical L-SSFs were constructed and were used to treat water from the same source as the I-SSFs. Half of the L-SSFs sand beds were composed of sterilized sand (sterile) from the industrial filters and the other half with sand taken directly from the same industrial filter (non-sterile). All filters were operated for 10 weeks, with the microbial community and water quality parameters sampled and analysed weekly. To characterize the microbial community phyla-specific qPCR assays and 454 pyrosequencing of the 16S rRNA gene were used in conjunction with an array of statistical techniques. The results demonstrate that it is possible to mimic both the water quality production and the structure of the microbial community of full-scale filters in the laboratory - at all levels of taxonomic classification except OTU - thus allowing comparison of LSSF experiments with full-scale units. Further, it was found that the sand type composing the filter bed (non-sterile or sterile), the water quality produced, the age of the filters and the depth of sand samples were all significant factors in explaining observed differences in the structure of the microbial consortia. This study is the first to the authors' knowledge that demonstrates that scaled-down slow sand filters can accurately reproduce the water quality and microbial consortia of full-scale slow sand filters.

X Demographics

X Demographics

The data shown below were collected from the profiles of 9 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 142 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 1%
United Kingdom 2 1%
Indonesia 1 <1%
Colombia 1 <1%
Germany 1 <1%
Chile 1 <1%
Unknown 134 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 36 25%
Student > Master 35 25%
Student > Bachelor 14 10%
Researcher 11 8%
Other 5 4%
Other 14 10%
Unknown 27 19%
Readers by discipline Count As %
Environmental Science 33 23%
Engineering 28 20%
Agricultural and Biological Sciences 18 13%
Biochemistry, Genetics and Molecular Biology 8 6%
Earth and Planetary Sciences 3 2%
Other 14 10%
Unknown 38 27%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 7. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 07 November 2017.
All research outputs
#5,329,726
of 25,374,917 outputs
Outputs from Water Research
#1,505
of 11,875 outputs
Outputs of similar age
#49,027
of 240,014 outputs
Outputs of similar age from Water Research
#4
of 45 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. Compared to these this one has done well and is in the 78th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 11,875 research outputs from this source. They receive a mean Attention Score of 5.0. This one has done well, scoring higher than 87% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 240,014 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 79% of its contemporaries.
We're also able to compare this research output to 45 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 91% of its contemporaries.