ArticleSensors (Basel, Switzerland)2020
Long-Term Evaluation and Calibration of Low-Cost Particulate Matter (PM) Sensor.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
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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.
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Who cites it
15 citing papers in PubMed.
- AQ-MultiCal: An Interactive No-Code Machine Learning Platform for Low-Cost Air Quality Sensor Calibration and Comparative Model Analysis.Sensors (Basel, Switzerland) · 2026Article
- Assessing the efficacy of a low-cost air pollution monitoring device for environmental and occupational exposure assessments.Environmental monitoring and assessment · 2025Article
- Evaluation of In-Situ Low-Cost Sensor Network in a Tropical Valley, Colombia.Sensors (Basel, Switzerland) · 2025Article
- Calibration Methods for Low-Cost Particulate Matter Sensors Considering Seasonal Variability.Sensors (Basel, Switzerland) · 2024Article
- Catastrophic impact of extreme 2019 Indonesian peatland fires on urban air quality and health.Communications earth & environment · 2024Article
- Low-Cost Particulate Matter Sensors for Monitoring Residential Wood Burning.Environmental science & technology · 2023Article
- Utilizing Low-Cost Sensors to Monitor Indoor Air Quality in Mongolian Gers.Sensors (Basel, Switzerland) · 2023Article
- Characterisation and calibration of low-cost PM sensors at high temporal resolution to reference-grade performance.Heliyon · 2023Article
- Low-Cost Sensor Node for Air Quality Monitoring: Field Tests and Validation of Particulate Matter Measurements.Sensors (Basel, Switzerland) · 2023Article
- Feasibility of low-cost particle sensor types in long-term indoor air pollution health studies after repeated calibration, 2019-2021.Scientific reports · 2022Article
- Evaluating the Performance of Using Low-Cost Sensors to Calibrate for Cross-Sensitivities in a Multipollutant Network.ACS ES&T engineering · 2022Article
- Evaluating the Performance of Low-Cost Air Quality Monitors in Dallas, Texas.International journal of environmental research and public health · 2022Article
- Practical Particulate Matter Sensing and Accurate Calibration System Using Low-Cost Commercial Sensors.Sensors (Basel, Switzerland) · 2021Article
- A Novel Method for Online Extraction of Small-Angle Scattering Pulse Signals from Particles Based on Variable Forgetting Factor RLS Algorithm.Sensors (Basel, Switzerland) · 2021Article
- A Review of Low-Cost Particulate Matter Sensors from the Developers' Perspectives.Sensors (Basel, Switzerland) · 2020Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Low-cost light scattering particulate matter (PM) sensors have been widely researched and deployed in order to overcome the limitations of low spatio-temporal resolution of government-operated beta attenuation monitor (BAM). However, the accuracy of low-cost sensors has been questioned, thus impeding their wide adoption in practice. To evaluate the accuracy of low-cost PM sensors in the field, a multi-sensor platform has been developed and co-located with BAM in Dongjak-gu, Seoul, Korea from 15 January 2019 to 4 September 2019. In this paper, a sample variation of low-cost sensors has been analyzed while using three commercial low-cost PM sensors. Influences on PM sensor by environmental conditions, such as humidity, temperature, and ambient light, have also been described. Based on this information, we developed a novel combined calibration algorithm, which selectively applies multiple calibration models and statistically reduces residuals, while using a prebuilt parameter lookup table where each cell records statistical parameters of each calibration model at current input parameters. As our proposed framework significantly improves the accuracy of the low-cost PM sensors (e.g., RMSE: 23.94 → 4.70 μ g/m 3 ) and increases the correlation (e.g., R 2 : 0.41 → 0.89), this calibration model can be transferred to all sensor nodes through the sensor network.
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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.