Evidence map›Paper›PMID 32820457›Full record

ArticleEnvironmental monitoring and assessment2020

Pollution zone identification research during ozone pollution processes.

Dongyue Li, Yilan Liao

Abstract read
PubMed Publisher
In one paragraph

Article in Environmental monitoring and assessment, 2020. 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

2 authors.

Dongyue LiThe State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China.ORCID http://orcid.org/0000-0001-8716-1381
Yilan LiaoThe State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China. liaoyl@lreis.ac.cn.

Funding

National Key R&D Program of China 2016YFC1302504National Natural Science Foundation of China 41471377, 41531179 and 41421001Program Grant in Fundamental Research from the Ministry of Science and Technology 2014FY121100
6 · The paper itself

Abstract

Identifying an ozone pollution zone during the pollution processes is significant for ozone pollution management and environmental health risk assessment. However, few studies have focused on ozone pollution zone identification during pollution processes. A spatial-temporal clustering framework for identifying pollution zones during ozone pollution processes was initially proposed in this study, and an ozone pollution process in China in May 2017 was selected as a case. The results showed that the framework can help selecting one more accurate method to identify the pollution zone according to the pollution characteristics of air pollution process. In addition, different ozone pollution zone identification methods work well in different scenarios: The self-organizing map (SOM) method was suitable for identifying the zone with the duration of pollution between 24 and 48 h, the image fusion based on wavelet transform (IFbWT) method for the zone with the duration of pollution over 48 h and the Apriori method for the zone with obvious boundaries between high-value and low-value ozone concentrations. The proposed procedure can also be applied to identify the pollution zone of the pollution process of other pollutants.

Indexed as

Air PollutantsAir PollutionChinaEnvironmental MonitoringOzoneAir PollutantsOzoneFrequency patternImage fusionPollution processPollution zoneSpatio-temporal clustering

Identifiers

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.