Evidence map›Paper›PMID 32212072›Full record

ArticleEnvironmental science and pollution research international2020

Assessment of surface water quality in Lushan: a world heritage sites in China.

Dongming Xie, Qinghua Huang, Shi Xu, Yangming Zhou, Guohong Zhou, Junsong Jia, Zaiyu Zhu

Abstract read
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In one paragraph

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. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Dongming XieSchool of Tourism, Jiangxi Science & Technology Normal University, Jiangxi, China.
Qinghua HuangSchool of Tourism, Jiangxi Science & Technology Normal University, Jiangxi, China.
Shi XuJiangxi Lushan National Nature Reserve Administration, Jiangxi, China.
Yangming ZhouKey Laboratory of Poyang Lake Wetland and Watershed Research, Ministry of Education, Jiangxi Normal University, Nanchang, China.
Guohong ZhouSchool of Tourism, Jiangxi Science & Technology Normal University, Jiangxi, China.
Junsong JiaKey Laboratory of Poyang Lake Wetland and Watershed Research, Ministry of Education, Jiangxi Normal University, Nanchang, China. jiaaniu@126.com.
Zaiyu ZhuSchool of Tourism, Jiangxi Science & Technology Normal University, Jiangxi, China. zzy191@163.com.

Funding

the National Natural Science Foundation of China 31360120
6 · The paper itself

Abstract

Surface water is one of the important landscape resources in tourist attractions. Due to tourism activities, the surface water quality (SWQ) in scenic was often damaged. An example of the Lushan Scenic, the SWQ, was analyzed and evaluated by water sampling and laboratory analysis methods. The results explained that the SWQ of Lushan Scenic was seriously damaged. The comprehensive index explained that the SWQ of seven sampling dots was from mild pollution to extreme pollution. The main pollutants were ammonia nitrogen, total nitrogen, and total phosphorus, and the TN and TP were the most serious. According to the data of tourists in 2017, the emergency water capacity stored by reservoirs was 32.5 days if there was no raining in Lushan Scenic. The main factors affecting the SWQ were tourism activities, such as tourists, hotels, restaurants, and other commercial activities, and pollutants discharged from domestic water were not completely treated in Lushan Scenic.

Indexed as

Water QualityChinaEnvironmental MonitoringNitrogenPhosphorusWater Pollutants, ChemicalNitrogenPhosphorusWater Pollutants, ChemicalEnvironmental qualityLushan ScenicPollutantsSurface water

Identifiers

What OpenQuestion holds

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Registered trials

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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.