Evidence map›Paper›PMID 37860138›Full record

ReviewFrontiers in microbiology2023

Research progress of novel bio-denitrification technology in deep wastewater treatment.

Shan Huang, Yuling Fu, Huimin Zhang, Chuqiao Wang, Chenglong Zou, Xiuguo Lu

Abstract readReview
In one paragraph

Review in Frontiers in microbiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Integrated biological and UV-Fenton processes for the treatment of automobile service station wastewater.Water science and technology : a journal of the International Association on Water Pollution Research · 2025
    Article
  3. 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

6 authors.

Shan HuangSchool of Civil Engineering and Architecture, East China Jiao Tong University, Nanchang, China.
Yuling FuSchool of Civil Engineering and Architecture, East China Jiao Tong University, Nanchang, China.
Huimin ZhangSchool of Civil Engineering and Architecture, East China Jiao Tong University, Nanchang, China.
Chuqiao WangSchool of Civil Engineering and Architecture, East China Jiao Tong University, Nanchang, China.
Chenglong ZouSchool of Civil Engineering and Architecture, East China Jiao Tong University, Nanchang, China.
Xiuguo LuSchool of Civil Engineering and Architecture, East China Jiao Tong University, Nanchang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Excessive nitrogen emissions are a major contributor to water pollution, posing a threat not only to the environment but also to human health. Therefore, achieving deep denitrification of wastewater is of significant importance. Traditional biological denitrification methods have some drawbacks, including long processing times, substantial land requirements, high energy consumption, and high investment and operational costs. In contrast, the novel bio-denitrification technology reduces the traditional processing time and lowers operational and maintenance costs while improving denitrification efficiency. This technology falls within the category of environmentally friendly, low-energy deep denitrification methods. This paper introduces several innovative bio-denitrification technologies and their combinations, conducts a comparative analysis of their denitrification efficiency across various wastewater types, and concludes by outlining the future prospects for the development of these novel bio-denitrification technologies.

Indexed as

coupling processdeep wastewater denitrificationdenitrification performancelow-carbonnovel bio-denitrification technology

Identifiers

PMID37860138
PMCPMC10582329

What OpenQuestion holds

Textmetadata
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.