Optimizing online groundwater monitoring in industrial parks using long-term high-frequency data.

Shuping Yi, Pizhu Huang, Yi Deng, Yi Liu, Hefei Huang, Zhiren Tian

Abstract read

In one paragraph

Article in iScience, 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

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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

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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

6 authors.

Shuping YiSchool of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518000, China.

Pizhu HuangSchool of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518000, China.

Yi DengSchool of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518000, China.

Yi LiuSchool of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518000, China.

Hefei HuangDepartment of Ecology and Environment of Jiangxi Province, Nanchang 330000, China.

Zhiren TianChina National Environmental Monitoring Centre, Beijing 100012, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Chemical industrial parks require groundwater monitoring strategies that can detect abnormal changes while limiting redundant measurements. We analyzed nearly three years of hourly data from eight online monitoring wells in a chemical industrial park. Indicator screening combined the normalized interquartile range with Spearman correlation analysis; monitoring frequency was optimized using downsampling and reconstruction-error metrics; and predictive validation employed ridge and random forest regression. Online monitoring captured sudden and persistent anomalies lasting 100-3,360 h. Ammonia nitrogen (NH

Indexed as

groundwater environmentindicator optimizationmonitoring frequencyonline monitoringpre-warning threshold

Identifiers

PMID42602994

DOI10.1016/j.isci.2026.117042

PMCPMC13475684

What OpenQuestion holds

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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.