Evidence map›Paper›PMID 35037152›Full record

ArticleEnvironmental science and pollution research international2022

A modified cyanobacteria prediction model based on cellular automata model using N and P concentration reverse data: a case study in Taihu Lake.

Fei Zhao, Sujin Zhang, Ruonan Chen, Liyun Xiao, Guize Luan, Siwen Feng, Zhiqiang Xie

Abstract read
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Article in Environmental science and pollution research international, 2022. 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
0.6field-weighted citation impact, top 34% of its field
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, 3 citations in OpenAlex.

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 at 2 institutions in 1 country.

Fei ZhaoSchool of Earth Sciences, Yunnan University, Kunming, 650500, China.
Sujin ZhangSchool of Earth Sciences, Yunnan University, Kunming, 650500, China.
Ruonan ChenKey Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, 100049, China.
Liyun XiaoSchool of Earth Sciences, Yunnan University, Kunming, 650500, China.
Guize LuanInstitute of International Rivers and Eco-Security, Yunnan University, Kunming, 650500, China.
Siwen FengInstitute of International Rivers and Eco-Security, Yunnan University, Kunming, 650500, China.
Zhiqiang XieSchool of Earth Sciences, Yunnan University, Kunming, 650500, China. xzq_2019@ynu.edu.cn.
Yunnan University · CNInstitute of Remote Sensing and Digital Earth · CN

Funding

National Natural Science Foundation of China (CN) 41961064
6 · The paper itself

Abstract

The problem of algal bloom caused by eutrophication has attracted global attention. Many scholars have studied the problem associated with algae bloom, but few have carried out dynamic monitoring, instead focusing on the formation mechanism of cyanobacteria. For our study of the Taihu Lake in China, we used Moderate-Resolution Imaging Spectroradiometer (MODIS) and Landsat remote sensing image data from 2017 to establish a prediction model. First, we used MODIS data to retrieve the concentration of N, P, and chlorophyll a in water. Then, we applied the analytic hierarchy process (AHP) model to the inversion results to construct the diffusion potential index. Finally, we used C# to compile the cellular automata (CA) model. We found that the distribution of cyanobacteria predicted by our method was consistent with the algal bloom situation of Taihu Lake in 2017. The results showed that the method effectively predicts the dynamic transfer of cyanobacteria from outbreak to diffusion in a short period of time, which can help decision-makers monitor lake health.

Indexed as

CyanobacteriaLakesCellular AutomataChinaChlorophyll AEnvironmental MonitoringEutrophicationPhosphorusChlorophyll APhosphorusCellular automataCyanobacteriaNitrogenPhosphorusPrediction modelTaihu Lake

Identifiers

PMID35037152
OpenAlexW4205199208

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.