Evidence map›Paper›PMID 37041802›Full record

ArticleNPJ clean water2023

Monitoring the impact of climate extremes and COVID-19 on statewise sentiment alterations in water pollution complaints.

Anqi Liu, Jonghun Kam, Sae Yun Kwon, Wanyun Shao

Open access · goldAbstract read
In one paragraph

Article in NPJ clean water, 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
1.4field-weighted citation impact, top 22% 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, 9 citations in OpenAlex.

  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

4 authors at 2 institutions in 2 countries.

Anqi LiuDivision of Environmental Science and Engineering, Pohang University of Science and Technology (POSTECH), Pohang, 37673 the Republic of Korea.ORCID 0000-0002-6857-0101
Jonghun KamDivision of Environmental Science and Engineering, Pohang University of Science and Technology (POSTECH), Pohang, 37673 the Republic of Korea.ORCID 0000-0002-7967-7705
Sae Yun KwonDivision of Environmental Science and Engineering, Pohang University of Science and Technology (POSTECH), Pohang, 37673 the Republic of Korea.
Wanyun ShaoDepartment of Geography, University of Alabama, Tuscaloosa, AL 35401 USA.ORCID 0000-0002-1609-7383
Pohang University of Science and Technology · KRUniversity of Alabama · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The COVID-19 pandemic and associated prevention policies can directly or indirectly alter the sentiment of individuals while registering water pollution complaints, but observational evidence remains limited. Here, we conducted a sentiment analysis on over 10,000 water pollution complaints from residents in Alabama, USA (2012-2021) to better understand how and to what extent COVID-19 has altered emotion (polarity score-based) and attitude (subjectivity) of water pollution complaints. We found that the 2017 state-wise drought significantly increased the percentage of negative water pollution complaints by +35%, with no significant alternation in attitude before the COVID-19 pandemic. Since COVID-19, the percentage of negative and subjective water pollution complaints significantly decreased and increased by -30 and +20%, respectively, and these sentiment alternations were maintained by 2021. This study provides a new direction for environmental governance and management, requiring a timely response to changes in the public's emotions and attitudes during the next climate extremes and pandemics.

Indexed as

Civil engineeringPsychology and behaviourWater resources

Identifiers

PMID37041802
PMCPMC10079150
OpenAlexW4362673462

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.