Evidence map›Paper›PMID 32989695›Full record

ArticleEnvironmental science and pollution research international2021

Variation of dissolved organic matter during excess sludge reduction in microbubble ozonation system.

Zhiyi Sun, Yuxiang Wang, Xiaoliang Chen, Nanwen Zhu, Haiping Yuan, Ziyang Lou

Abstract read
PubMed Publisher
In one paragraph

Article in Environmental science and pollution research international, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Progress of researches on synergetic mechanism of micro-nano-bubble-assisted ozonation.Water science and technology : a journal of the International Association on Water Pollution Research · 2025
    Review
  2. Review
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.

Zhiyi SunShanghai Engineering Research Center of Solid Waste Treatment and Recycling, School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.
Yuxiang WangShanghai Engineering Research Center of Solid Waste Treatment and Recycling, School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.
Xiaoliang ChenShanghai Solid Waste Management Center, Shanghai, 200235, China.
Nanwen ZhuShanghai Engineering Research Center of Solid Waste Treatment and Recycling, School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.
Haiping YuanShanghai Engineering Research Center of Solid Waste Treatment and Recycling, School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.
Ziyang LouShanghai Engineering Research Center of Solid Waste Treatment and Recycling, School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China. louworld12@sina.com.

Funding

National Natural Science Foundation of China No. 51678357National Natural Science Foundation of China U1662128Shanghai environmental protection bureau 2017 [47]Shanghai Science and Technology Commission 17DZ2252100
6 · The paper itself

Abstract

Sewage sludge is the major by-product of wastewater treatment plants, and about 30% readily biodegradable organic matters might be reused through the mass reduction process, which could be also reduced the disposal fee. In this study, the microbubble ozonation (MB-O

Indexed as

OzoneSewageHumic SubstancesMicrobubblesWaste Disposal, FluidHumic SubstancesOzoneSewageDissolved organic matterEEM-FIR spectrumMicrobubble ozonationPartial size distributionSludge solubilization

Identifiers

PMID32989695

What OpenQuestion holds

Textmetadata
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.