ArticleACS ES&T air2024
Air Quality Sensor Experts Convene: Current Quality Assurance Considerations for Credible Data.
Article in ACS ES&T air, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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.
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.
Who cites it
10 citing papers in PubMed.
- Review
- Field Evaluation of Community Condensation Particle Counters, Optical Particle Counters, and a Low-Cost Particle Sensor.Aerosol science and technology : the journal of the American Association for Aerosol Research · 2026Article
- Air Sensor Network Analysis Tool: R-Shiny Application.Atmosphere · 2025Article
- Article
- Article
- Article
- Quality Assurance and Control of Air Sensors: EPA Efforts to Advance Information Sharing, Education, and Tools to Improve Air Sensor Data Quality.EM (Pittsburgh, Pa.) · 2025Article
- SENTINEL: A Shiny App for Processing and Analysis of Fenceline Sensor Data.Environmental modelling & software : with environment data news · 2025Article
- Evaluation of Long-Term Performance of Six PMSensors (Basel, Switzerland) · 2025Article
- A Scalable Calibration Method for Enhanced Accuracy in Dense Air Quality Monitoring Networks.Environmental science & technology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
22 authors.
Funding
Abstract
Air sensors can provide valuable non-regulatory and supplemental data as they can be affordably deployed in large numbers and stationed in remote areas far away from regulatory air monitoring stations. Air sensors have inherent limitations that are critical to understand before collecting and interpreting the data. Many of these limitations are mechanistic in nature, which will require technological advances. However, there are documented quality assurance (QA) methods to promote data quality. These include laboratory and field evaluation to quantitatively assess performance, the application of corrections to improve precision and accuracy, and active management of the condition or state of health of deployed air quality sensors. This paper summarizes perspectives presented at the U.S. Environmental Protection Agency's 2023 Air Sensors Quality Assurance Workshop (https://www.epa.gov/air-sensor-toolbox/quality-assurance-air-sensors#QAworkshop) by stakeholders (e.g., manufacturers, researchers, air agencies) and identifies the most pressing needs. These include QA protocols, streamlined data processing, improved total volatile organic compound (TVOC) data interpretation, development of speciated VOC sensors, and increased documentation of hardware and data handling. Community members using air sensors need training and resources, timely data, accessible QA approaches, and shared responsibility with other stakeholders. In addition to identifying the vital next steps, this work provides a set of common QA and QC actions aimed at improving and homogenizing air sensor QA that will allow stakeholders with varying fields and levels of expertise to effectively leverage air sensor data to protect human health.
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Identifiers
What OpenQuestion holds
Registered trials
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.