Evidence map›Paper›PMID 24919129›Full record

ArticleInternational journal of environmental research and public health2014

Identification and assessment of potential water quality impact factors for drinking-water reservoirs.

Qing Gu, Jinsong Deng, Ke Wang, Yi Lin, Jun Li, Muye Gan, Ligang Ma, Yang Hong

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2014. 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.

  1. Review
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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

8 authors.

Qing GuInstitute of Applied Remote Sensing & Information Technology, Zhejiang University, Hangzhou 310058, Zhejiang, China. funny@zju.edu.cn.
Jinsong DengInstitute of Applied Remote Sensing & Information Technology, Zhejiang University, Hangzhou 310058, Zhejiang, China. jsong_deng@zju.edu.cn.
Ke WangInstitute of Applied Remote Sensing & Information Technology, Zhejiang University, Hangzhou 310058, Zhejiang, China. kwang@zju.edu.cn.
Yi LinInstitute of Applied Remote Sensing & Information Technology, Zhejiang University, Hangzhou 310058, Zhejiang, China. linyi11@zju.edu.cn.
Jun LiInstitute of Applied Remote Sensing & Information Technology, Zhejiang University, Hangzhou 310058, Zhejiang, China. lijun_more@126.com.
Muye GanInstitute of Applied Remote Sensing & Information Technology, Zhejiang University, Hangzhou 310058, Zhejiang, China. ganmuye@zju.edu.cn.
Ligang MaInstitute of Applied Remote Sensing & Information Technology, Zhejiang University, Hangzhou 310058, Zhejiang, China. 11114054@zju.edu.cn.
Yang HongSchool of Civil Engineering and Environmental Sciences and School of Meteorology, University of Oklahoma, Norman, OK 73019, USA. yanghong@ou.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Various reservoirs have been serving as the most important drinking water sources in Zhejiang Province, China, due to the uneven distribution of precipitation and severe river pollution. Unfortunately, rapid urbanization and industrialization have been continuously challenging the water quality of the drinking-water reservoirs. The identification and assessment of potential impacts is indispensable in water resource management and protection. This study investigates the drinking water reservoirs in Zhejiang Province to better understand the potential impact on water quality. Altogether seventy-three typical drinking reservoirs in Zhejiang Province encompassing various water storage levels were selected and evaluated. Using fifty-two reservoirs as training samples, the classification and regression tree (CART) method and sixteen comprehensive variables, including six sub-sets (land use, population, socio-economy, geographical features, inherent characteristics, and climate), were adopted to establish a decision-making model for identifying and assessing their potential impacts on drinking-water quality. The water quality class of the remaining twenty-one reservoirs was then predicted and tested based on the decision-making model, resulting in a water quality class attribution accuracy of 81.0%. Based on the decision rules and quantitative importance of the independent variables, industrial emissions was identified as the most important factor influencing the water quality of reservoirs; land use and human habitation also had a substantial impact on water quality. The results of this study provide insights into the factors impacting the water quality of reservoirs as well as basic information for protecting reservoir water resources.

Indexed as

Water QualityChinaDrinking WaterEnvironmental MonitoringWater PollutionWater SupplyDrinking Water

Identifiers

PMID24919129
PMCPMC4078566

What OpenQuestion holds

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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.