Evidence map›Paper›PMID 41784679›Full record

ArticleEnvironmental monitoring and assessment2026

Spatial analysis of air pollution and cancer prevalence in Texas: a machine learning approach using satellite remote sensing data.

Fangchao Dong, Muhammad Tauhidur Rahman, Hao Chen

Abstract read
In one paragraph

Article in Environmental monitoring and assessment, 2026. 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

3 authors.

Fangchao DongGeospatial Information Sciences Program, School of Economic, Political and Policy Sciences, University of Texas at Dallas, Richardson, TX, 75080-3021, USA. fangchao.dong@utdallas.edu.
Muhammad Tauhidur RahmanGeospatial Information Sciences Program, School of Economic, Political and Policy Sciences, University of Texas at Dallas, Richardson, TX, 75080-3021, USA. mtr@utdallas.edu.
Hao ChenGeospatial Information Sciences Program, School of Economic, Political and Policy Sciences, University of Texas at Dallas, Richardson, TX, 75080-3021, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cancer prevalence in the world has been attributed to exposure to air pollutants. However, spatial analyses utilizing remote sensing data have been limited. This study combined satellite-derived measurements of eight air pollutants (SO

Indexed as

Air PollutantsAir PollutionEnvironmental MonitoringMachine LearningNeoplasmsClustering AlgorithmsHumansPrevalenceRemote Sensing TechnologySpatial AnalysisTexasAir PollutantsAir pollutantsCancer prevalenceMachine learningPermutation importanceRemote sensingSpatial clustering

Identifiers

PMID41784679
PMCPMC12963168

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.