Evidence map›Paper›PMID 38535962›Full record

ArticleToxics2024

Identifying the Local Influencing Factors of Arsenic Concentration in Suburban Soil: A Multiscale Geographically Weighted Regression Approach.

Yuanli Zhu, Bo Liu, Gui Jin, Zihao Wu, Dongyan Wang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Toxics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

5 authors.

Yuanli ZhuSchool of Public Policy & Management, China University of Mining and Technology, Xuzhou 221116, China.
Bo LiuSchool of Public Policy & Management, China University of Mining and Technology, Xuzhou 221116, China.
Gui JinSchool of Public Policy & Management, China University of Mining and Technology, Xuzhou 221116, China.ORCID 0000-0002-2496-458X
Zihao WuSchool of Public Policy & Management, China University of Mining and Technology, Xuzhou 221116, China.
Dongyan WangCollege of Earth Sciences, Jilin University, Changchun 130061, China.

Funding

Dongyan Wang Grant No. 20170101076JC
6 · The paper itself

Abstract

Exploring the local influencing factors and sources of soil arsenic (As) is crucial for reducing As pollution, protecting soil ecology, and ensuring human health. Based on geographically weighted regression (GWR), multiscale GWR (MGWR) considers the different influence ranges of explanatory variables and thus adopts an adaptative bandwidth. It is an effective model in many fields but has not been used in exploring local influencing factors and sources of As. Therefore, using 200 samples collected from the northeastern black soil zone of China, this study examined the effectiveness of MGWR, revealed the spatial non-stationary relationship between As and environmental variables, and determined the local impact factors and pollution sources of As. The results showed that 49% of the samples had arsenic content exceeding the background value, and these samples were mainly distributed in the central and southern parts of the region. MGWR outperformed GWR with the adaptative bandwidth, with a lower Moran's I of residuals and a higher R

Indexed as

adaptative bandwidtharseniclocal influencing factormultiscale geographically weighted regressionspatial heterogeneity

Identifiers

PMID38535962
PMCPMC10976007

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.