Evidence map›Paper›PMID 37637175›Full record

ArticlePeerJ2023

Water lettuce (

Ngan Nguyen Vo Chau, Thao Huynh Van, Thuan Nguyen Cong, Lavane Kim, Dan Van Pham

Abstract read
In one paragraph

Article in PeerJ, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Ngan Nguyen Vo ChauDepartment of Water Resources, Can Tho University, Can Tho City, Vietnam.ORCID 0000-0003-4717-6575
Thao Huynh VanDepartment of Environmental Sciences, Can Tho University, Can Tho City, Vietnam.ORCID 0000-0001-6582-9061
Thuan Nguyen CongDepartment of Environmental Sciences, Can Tho University, Can Tho City, Vietnam.
Lavane KimDepartment of Environmental Engineering, Can Tho University, Can Tho City, Vietnam.
Dan Van PhamCenter for Technology Development and Agricultural Extension, Vietnam Academy of Agricultural Sciences, Ha Noi, Vietnam.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Aquatic plants play a crucial role in nature-based wastewater treatment and provide a promising substrate for renewable energy production using anaerobic digestion (AD) technology. This study aimed to examine the contaminant removal from AD effluent by water lettuce (WL) and produce biogas from WL biomass co-digested with pig dung (PD) in a farm-scale biogas digester. Methods: The first experiment used styrofoam boxes containing husbandry AD effluent. WLs were initially arranged in 50%, 25%, 12.5%, and 0% surface coverage. Each treatment was conducted in five replicates under natural conditions. In the second experiment, WL biomass was co-digested with PD into an existing anaerobic digester to examine biogas production on a farm scale. Results: Over 30 days, the treatment efficiency of TSS, BOD5, COD, TKN, and TP in the effluent was 93.75-97.66%, 76.63-82.56%, 76.78-82.89%, 61.75-63.75%, and 89.00-89.57%, respectively. Higher WL coverage increased the pollutant elimination potential. The WL biomass doubled after 12 days for all treatments. In the farm-scale biogas production, the biogas yield varied between 190.6 and 292.9 L kg VS Conclusions: WL removed AD effluent nutrients effectively through a phytoremediation system and generated significant biomass for renewable energy production in a farm-scale model.

Indexed as

AraceaeEnvironmental PollutantsAnimalsBiofuelsBiomassFarmsSwineBiofuelsEnvironmental PollutantsBiodegradationHusbandry wastePhytoremediationWastewater treatmentWater lettuce

Identifiers

PMID37637175
PMCPMC10452623

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.