Evidence map›Paper›PMID 41170403›Full record

ArticleMethodsX2025

Tracking microplastic pathways: Real-time IoT monitoring for water quality and public health.

Priya Govindarajan, Veena Krishna Shahapuram, Prajanya ShivajiRao Ganesh, Vachana Manjunatha Basavalingappa, Keerthana Yenneholekoppal Balarama, Prakash Kumar Sarangi, Jun Young Cheong, Vinod Vellora Thekkae Padil

Abstract read
In one paragraph

Article in MethodsX, 2025. 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

8 authors.

Priya GovindarajanDepartment of Computer Science, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Mysuru, India, 570026.
Veena Krishna ShahapuramDepartment of Computer Science, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Mysuru, India, 570026.
Prajanya ShivajiRao GaneshDepartment of Computer Science, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Mysuru, India, 570026.
Vachana Manjunatha BasavalingappaDepartment of Computer Science, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Mysuru, India, 570026.
Keerthana Yenneholekoppal BalaramaDepartment of Computer Science, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Mysuru, India, 570026.
Prakash Kumar SarangiCollege of Agriculture, Central Agricultural University, Imphal, 795004, Manipur, India.
Jun Young CheongJames Watt School of Engineering, University of Glasgow, Glasgow, G12 8QQ, UK.
Vinod Vellora Thekkae PadilSchool of Nanoscience (SNS), Central University of Gujarat (CUG), Kundhela, 391107, Vadodara, Gujarat, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Microplastics are a growing environmental threat due to their pervasive presence in aquatic ecosystems and their risks to both ecology and public health. Conventional monitoring methods, such as microscope-based analysis, are costly, labor-intensive, and impractical for large-scale deployment. To overcome these limitations, the study has proposed a cost-effective, IoT-enabled system for real-time detection and an algorithm to extract turbidity-based features for detection. The study introducess the Turbidity Enhanced Microplastic Tracker (TEMPT)-a cost-effective, IoT-enabled system for real-time detection. TEMPT integrates a turbidity sensor with a microcontroller, enabling scalable monitoring with ultra-low power consumption for long-term use in diverse water bodies. Complementing the hardware, the Turbo-Enhanced Tracking Microplastic for Water Sanity (TETM-Water) algorithm extracts turbidity-based features to ensure robust detection even under noisy conditions. Unlike standard techniques that typically yield below 85 % accuracy and high error rates, TETM-Water achieves 91.47 % accuracy with a 5.40 % error rate, demonstrating superior reliability. Key Highlights of the study are - IoT-enabled turbidity sensing and real-time data processing, Low-power hardware optimized for long-term field deployment, TETM-Water algorithm for accurate and noise-resilient detection.TEMPT provides actionable insights for policymakers and supports UN SDGs 3 and 6, advancing cleaner water and better health worldwide.

Indexed as

AccuracyIoTMicroplasticsSDGsTurbidity

Identifiers

PMID41170403
PMCPMC12569841

What OpenQuestion holds

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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.