Development and machine learning-based calibration of low-cost multiparametric stations for the measurement of CO

R Biagi, M Ferrari, S Venturi, M Sacco, G Montegrossi, F Tassi

Abstract read

In one paragraph

Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it

0cells of the map it votes in

4citing 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

4 citing papers in PubMed.

Comparative Evaluation of Machine Learning and Hyperparameter Optimization Methods for Low-Cost COSensors (Basel, Switzerland) · 2026
Article
Metrological Characterization of Low-Cost COSensors (Basel, Switzerland) · 2026
Article
Machine Learning-Based Calibration and Performance Evaluation of Low-Cost Internet of Things Air Quality Sensors.Sensors (Basel, Switzerland) · 2025
Article
Monitoring air pollutants in urbanized hydrothermal areas: challenges and benefits of traditional measurement strategies.Environmental geochemistry and health · 2025
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

6 authors.

R BiagiDepartment of Earth Sciences, University of Florence, Via G. La Pira 4, 50121, Firenze, Italy.

M FerrariDepartment of Earth Sciences, University of Florence, Via G. La Pira 4, 50121, Firenze, Italy.

S VenturiDepartment of Earth Sciences, University of Florence, Via G. La Pira 4, 50121, Firenze, Italy.

M SaccoDepartment of Physics and Astronomy, University of Florence, Via Sansone 1, 50019, Sesto Fiorentino, Firenze, Italy.

G MontegrossiInstitute of Geosciences and Earth Resources (IGG), National Research Council of Italy (CNR), Via G. La Pira 4, 50121, Firenze, Italy.

F TassiDepartment of Earth Sciences, University of Florence, Via G. La Pira 4, 50121, Firenze, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The pressing issue of atmospheric pollution has prompted the exploration of affordable methods for measuring and monitoring air contaminants as complementary techniques to standard methods, able to produce high-density data in time and space. The main challenge of this low-cost approach regards the in-field accuracy and reliability of the sensors. This study presents the development of low-cost stations for high-time resolution measurements of CO

Indexed as

Air qualityGreenhouse gasesLow-cost sensorsMachine learning

Identifiers

PMID38720758

DOI10.1016/j.heliyon.2024.e29772

PMCPMC11076643

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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.