Evidence map›Paper›PMID 33389583›Full record

ArticleEnvironmental science and pollution research international2021

Response of non-point source pollution to landscape pattern: case study in mountain-rural region, China.

Wuhua Li, Xiangju Cheng, Yu Zheng, Chengguang Lai, David J Sample, Dantong Zhu, Zhaoli Wang

Abstract read
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In one paragraph

Article in Environmental science and pollution research international, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

7 authors.

Wuhua LiState Key Laboratory of Subtropical Building Science, South China University of Technology, Guangzhou, 510640, China.
Xiangju ChengState Key Laboratory of Subtropical Building Science, South China University of Technology, Guangzhou, 510640, China.
Yu ZhengGuangdong Hydropower Planning & Design Institute, Guangzhou, 510635, China.
Chengguang LaiState Key Laboratory of Subtropical Building Science, South China University of Technology, Guangzhou, 510640, China. laichg@scut.edu.cn.
David J SampleDepartment of Biological System Engineering, Virginia Polytechnic Institute and State University, Virginia Beach, VA, 23455, USA.
Dantong ZhuState Key Laboratory of Subtropical Building Science, South China University of Technology, Guangzhou, 510640, China. ctzhudantong@mail.scut.edu.cn.ORCID http://orcid.org/0000-0001-5022-8108
Zhaoli WangState Key Laboratory of Subtropical Building Science, South China University of Technology, Guangzhou, 510640, China.

Funding

China Postdoctoral Science Foundation 2020M672635Fundamental Research Funds for the Central Universities 2019JQ06GuangXi Key R&D Program AB18221108National Natural Science Foundation of China 51579106
6 · The paper itself

Abstract

Landscape patterns have a substantial effect on non-point source (NPS) pollution in watersheds. Facilitating sustainable development of mountain-rural areas is a major priority for China. Knowledge of the impacts of various landscapes on water quality in these areas is critical to meeting environmental goals. This study applied the Soil and Water Assessment Tool (SWAT) to create a hydrologic and water quality model of the study watershed; then, the relationship between water quality and landscape patterns was investigated using multiple linear regression and redundancy analysis. The results show that the western sub-basins had higher nitrogen pollution loads, and the total nitrogen concentration reached a maximum value of 3.91 mg/L; the eastern sub-basins had a higher pollution load of phosphorous featured by maximum total phosphorous concentration of 2.15 mg/L. The water quality of the entire watershed in all scenarios tended to deteriorate over time. Landscape metrics accounted for 81.7% of the total variation in pollutant indicators. The percentage of forest landscape was negatively correlated with NPS pollution, while other types of landscape showed a positive correlation. The patch density, landscape shape index, and largest patch index of urban and agricultural lands were negatively correlated with pollutant concentrations. Upland landscapes contributed more pollutants than paddy fields. Some measures, e.g., returning grassland and farmland to forest in steep regions and replacing upland crops with paddy fields, were recommended for mitigating NPS pollution in the study watershed.

Indexed as

Non-Point Source PollutionWater Pollutants, ChemicalChinaEnvironmental MonitoringNitrogenRiversWater QualityNitrogenWater Pollutants, ChemicalLand use/land cover changeMigration regularityNon-point source pollutionSWATWatershed management

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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.