Evidence map›Paper›PMID 34988785›Full record

ArticleEnvironmental science and pollution research international2022

Seasonal and vegetational variations of culturable bacteria concentrations in air from urban forest parks: a case study in Hunan, China.

Zhen-Ni Liao, Hui-Juan Xu, Jiaojiao Ma, Maojuan Li, Caisheng He, Qiongrui Zhang, Songjun Xu

Erratum issuedAbstract read
PubMed Publisher
In one paragraph

Article in Environmental science and pollution research international, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.6field-weighted citation impact, top 36% of its field
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, 6 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors at 3 institutions in 1 country.

Zhen-Ni LiaoSchool of Geography, South China Normal University, Guangzhou, 510631, China.
Hui-Juan XuCollege of National Resources & Environment, South China Agricultural University, Guangzhou, 510642, China.
Jiaojiao MaSchool of Geography, South China Normal University, Guangzhou, 510631, China.
Maojuan LiChenzhou Institute of Forestry, Chenzhou, 423000, China.
Caisheng HeChenzhou Institute of Forestry, Chenzhou, 423000, China.
Qiongrui ZhangSchool of Geography, South China Normal University, Guangzhou, 510631, China.
Songjun XuSchool of Geography, South China Normal University, Guangzhou, 510631, China. xusj@scnu.edu.cn.
Chenzhou First People's Hospital · CNSouth China Normal University · CNSouth China Agricultural University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

It is important to investigate the airborne bacterial air quality in urban forest parks as tree bacteriostasis practices are being increasingly advocated as measures to improve the air quality and public health in urban green spaces around the world. The aim of the study was to quantitatively investigate airborne culturable bacteria (ACB) concentration levels based on field measurements in every season in five selected forest communities and the uncovered space in an urban forest park, as well as the effects of several factors on the culturability of airborne bacteria. Results suggested that the airborne bacterial levels of all the forest communities reached the clean air quality standard with regard to the airborne bacteria content, with the highest concentration of ACB showing in the uncovered space (1658 ± 1298 CFU/m

Indexed as

Air MicrobiologyParks, RecreationalBacteriaChinaEnvironmental MonitoringForestsSeasonsTreesAntimicrobial capacityConcentrationForest airborne bacteriaInfluencing factorsUrban forest park communityVegetation specie

Identifiers

PMID34988785
OpenAlexW4205316732

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.