Factors Influencing the Spatial Distribution of Microplastics in Lakes with the Example of Dianchi Lake

The spatial law and influencing factors of microplastics in lakes are an important part of microplastic research. This study focused on exploring the influences of factors such as the underwater slope, water depth, and pollution source/shore distance of a lake on the spatial distribution of lake MPs (microplastics). The relationships between the underwater slope, other factors, and the concentration of MPs were analyzed. The results showed that the average abundance of MPs in Dianchi Lake was 129 n/m3, and the spatial distribution pattern was higher in the lake center and lower in the lake shore. The correlation analysis showed that MP abundance was not significantly associated with distance to pollution sources or water depth, but it was significantly correlated with slope (p < 0.05) and offshore distance (p < 0.01). There was a significant negative correlation between the abundance of MPs and the underwater slope. The greater the underwater slope, the lower the abundance of MPs; the smaller the underwater slope, the higher the abundance of MPs. There was a significant positive correlation between the abundance of MPs and offshore distance. The abundance of MPs increased with increasing offshore distance. These new discoveries will help us better understand the spatial patterns of MPs in lakes.

Authors

Institutions

Publication Details

Journal
Water
Published
2025-08-11
DOI
https://doi.org/10.3390/w17162377
Citations
1
Primary Topic
Microplastics and Plastic Pollution
Type
article
Field-Weighted Citation Impact
0.50

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Factors Influencing the Spatial Distribution of Microplastics in Lakes with the Example of Dianchi Lake

Hui Zhao, Yuejiao Huang, Quan Zhang, Chunnuan Deng et al.
1 citations
Water
Microplastics and Plastic Pollution
0.50
article

Factors Influencing the Spatial Distribution of Microplastics in Lakes with the Example of Dianchi Lake

Hui Zhao, Yuejiao Huang, Quan Zhang, Chunnuan Deng, Yao Hu
article en
1 citations

Abstract

The spatial law and influencing factors of microplastics in lakes are an important part of microplastic research. This study focused on exploring the influences of factors such as the underwater slope, water depth, and pollution source/shore distance of a lake on the spatial distribution of lake MPs (microplastics). The relationships between the underwater slope, other factors, and the concentration of MPs were analyzed. The results showed that the average abundance of MPs in Dianchi Lake was 129 n/m3, and the spatial distribution pattern was higher in the lake center and lower in the lake shore. The correlation analysis showed that MP abundance was not significantly associated with distance to pollution sources or water depth, but it was significantly correlated with slope (p < 0.05) and offshore distance (p < 0.01). There was a significant negative correlation between the abundance of MPs and the underwater slope. The greater the underwater slope, the lower the abundance of MPs; the smaller the underwater slope, the higher the abundance of MPs. There was a significant positive correlation between the abundance of MPs and offshore distance. The abundance of MPs increased with increasing offshore distance. These new discoveries will help us better understand the spatial patterns of MPs in lakes.

WaterVol. 17(16)
Yunnan Normal University (CN), China Geological Survey (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 36%
Microplastics and Plastic Pollution
0.50
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.