Evidence map›Paper›PMID 31797271›Full record

ArticleEnvironmental science and pollution research international2020

Risk grade assessment of sudden water pollution based on analytic hierarchy process and fuzzy comprehensive evaluation.

Hao Zhang, Wenjun Li, Pingping Miao, Bowen Sun, Fanqing Kong

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Article in Environmental science and pollution research international, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

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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

5 authors.

Hao ZhangCollege of Water Resources and Hydrology, Hohai University, Nanjing, China.
Wenjun LiWater Resources Protection Bureau of Haihe River Basin, Tianjin, China.
Pingping MiaoWater Resources Protection Bureau of Haihe River Basin, Tianjin, China.
Bowen SunState Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, Tianjin, China. bwsun@tju.edu.cn.
Fanqing KongHaihe River Water Environmental Monitoring Center, Tianjin, China.

Funding

Major Science and Technology Program for Water Pollution Control and Treatment 2018ZX07101005
6 · The paper itself

Abstract

Sudden water pollution is a global environmental issue. On a large spatial scale, any pollution source may cause water pollution incidents. Dividing a large watershed into several units and giving each unit a different level would be favorable for managing sudden water pollution incidents. To scientifically divide an area into units and determine their risk grade of sudden water pollution, data of pollution sources were collected, and an area was divided into risk assessment units of sudden water pollution. The risk grade assessment was conducted following the analytic hierarchy process (AHP) and fuzzy comprehensive evaluation method. The technology combined with the AHP and fuzzy comprehensive evaluation (F-AHP) can generate the hydrology, pollution source, natural geography, and socioeconomic characteristics of each unit and provide a risk grade evaluation. A risk grade assessment was conducted using the Yongding River as a case study. The results show that the areas with high sudden water pollution risk were mainly distributed in Shanxi Datong and Hebei Zhangjiakou. Sufficient control of pollution sources in production processes and the establishment of necessary measures to strengthen inspections should reduce the risk of sudden water pollution.

Indexed as

ChinaEnvironmental MonitoringFuzzy LogicGeographyGRADE ApproachHydrologyRisk AssessmentRiversWater PollutionAHPFuzzy comprehensive evaluationRisk grade assessmentSudden water pollution

Identifiers

What OpenQuestion holds

Textmetadata
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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.