Evidence map›Paper›PMID 40006257›Full record

ArticleSensors (Basel, Switzerland)2025

A Systematic Study of Popular Software Packages and AI/ML Models for Calibrating In Situ Air Quality Data: An Example with Purple Air Sensors.

Seren Smith, Theodore Trefonides, Anusha Srirenganathan Malarvizhi, Shyra LaGarde, Jiakang Liu, Xiaoguo Jia, Zifu Wang, Jacob Cain, Thomas Huang, Mohammad Pourhomayoun and 7 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. AI Methods in Sensor Calibration.Sensors (Basel, Switzerland) · 2026
    Review
  2. PurpleAir Sensor Deployment Trends and Uncertainties.Sensors (Basel, Switzerland) · 2026
    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

17 authors.

Seren SmithNSF Spatiotemporal Innovation Center, George Mason University, 4400 University Dr., Fairfax, VA 22030, USA.ORCID 0000-0002-0354-7381
Theodore TrefonidesNSF Spatiotemporal Innovation Center, George Mason University, 4400 University Dr., Fairfax, VA 22030, USA.
Anusha Srirenganathan MalarvizhiNSF Spatiotemporal Innovation Center, George Mason University, 4400 University Dr., Fairfax, VA 22030, USA.
Shyra LaGardeNSF Spatiotemporal Innovation Center, George Mason University, 4400 University Dr., Fairfax, VA 22030, USA.
Jiakang LiuNSF Spatiotemporal Innovation Center, George Mason University, 4400 University Dr., Fairfax, VA 22030, USA.
Xiaoguo JiaNSF Spatiotemporal Innovation Center, George Mason University, 4400 University Dr., Fairfax, VA 22030, USA.
Zifu WangNSF Spatiotemporal Innovation Center, George Mason University, 4400 University Dr., Fairfax, VA 22030, USA.ORCID 0000-0002-7183-5166
Jacob CainNSF Spatiotemporal Innovation Center, George Mason University, 4400 University Dr., Fairfax, VA 22030, USA.
Thomas HuangNASA Jet Propulsion Laboratory, 4800 Oak Grove Dr., Pasadena, CA 91011, USA.
Mohammad PourhomayounDepartment of Computer Science, California State University, 1250 Bellflower Blvd, Long Beach, CA 90840, USA.ORCID 0000-0002-0539-7487
Grace LlewellynNASA Jet Propulsion Laboratory, 4800 Oak Grove Dr., Pasadena, CA 91011, USA.
Wai PhyoNASA Jet Propulsion Laboratory, 4800 Oak Grove Dr., Pasadena, CA 91011, USA.
Sina HasheminassabNASA Jet Propulsion Laboratory, 4800 Oak Grove Dr., Pasadena, CA 91011, USA.
Joe RobertsNASA Jet Propulsion Laboratory, 4800 Oak Grove Dr., Pasadena, CA 91011, USA.
Kevin MarlisNASA Jet Propulsion Laboratory, 4800 Oak Grove Dr., Pasadena, CA 91011, USA.
Daniel Q DuffyNASA Goddard Space Flight Center, Greenbelt, MD 220771, USA.
Chaowei YangNSF Spatiotemporal Innovation Center, George Mason University, 4400 University Dr., Fairfax, VA 22030, USA.ORCID 0000-0001-7768-4066

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accurate air pollution monitoring is critical to understand and mitigate the impacts of air pollution on human health and ecosystems. Due to the limited number and geographical coverage of advanced, highly accurate sensors monitoring air pollutants, many low-cost and low-accuracy sensors have been deployed. Calibrating low-cost sensors is essential to fill the geographical gap in sensor coverage. We systematically examined how different machine learning (ML) models and open-source packages could help improve the accuracy of particulate matter (PM) 2.5 data collected by Purple Air sensors. Eleven ML models and five packages were examined. This systematic study found that both models and packages impacted accuracy, while the random training/testing split ratio (e.g., 80/20 vs. 70/30) had minimal impact (0.745% difference for R

Indexed as

Air PollutantsAir PollutionEnvironmental MonitoringMachine LearningSoftwareCalibrationHumansParticulate MatterAir PollutantsParticulate MatterAI/MLair qualitycalibrationenvironmentmodel accuracyparticulate mattersensor calibrationsensors

Identifiers

PMID40006257
PMCPMC11859157

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.