ArticleSensors (Basel, Switzerland)2021
Developing Relative Humidity and Temperature Corrections for Low-Cost Sensors Using Machine Learning.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 41 citations in OpenAlex.
- Limitations of Environmental Extrapolation in Low-Cost Carbon Monoxide and Fine Particulate Matter Sensors.Sensors (Basel, Switzerland) · 2026Article
- AS7341 Spectral Sensor with Machine Learning for Non-Contact Temperature Monitoring in Electrolytic-Plasma Hardening.Sensors (Basel, Switzerland) · 2026Article
- Improving Low-Cost Optical PM Sensor Accuracy in Humid Conditions via Aerosol Liquid Water Estimation Using U.S. EPA CSN Data.ACS ES&T air · 2026Article
- Utility of low-cost sensor measurement for predicting ambient PMEnvironmental science: atmospheres · 2025Article
- Innovative Air-Preconditioning Method for Accurate Particulate Matter Sensing in Humid Environments.Sensors (Basel, Switzerland) · 2024Article
- Calibration and Inter-Unit Consistency Assessment of an Electrochemical Sensor System Using Machine Learning.Sensors (Basel, Switzerland) · 2024Article
- Data-Driven Machine Learning Calibration Propagation in A Hybrid Sensor Network for Air Quality Monitoring.Sensors (Basel, Switzerland) · 2023Article
- Calibration of a Stereoscopic Vision System in the Presence of Errors in Pitch Angle.Sensors (Basel, Switzerland) · 2022Article
- Long-Term Assessment of a Set of COSensors (Basel, Switzerland) · 2022Article
- Building Low-Cost Sensing Infrastructure for Air Quality Monitoring in Urban Areas Based on Fog Computing.Sensors (Basel, Switzerland) · 2022Article
- Applications of artificial intelligence in the field of air pollution: A bibliometric analysis.Frontiers in public health · 2022Article
- The Simulation of In-Situ Groundwater Detector Response as a Means of Identifying Beta Emitting Radionuclides by Linear Regression Analysis.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
Existing government air quality monitoring networks consist of static measurement stations, which are highly reliable and accurately measure a wide range of air pollutants, but they are very large, expensive and require significant amounts of maintenance. As a promising solution, low-cost sensors are being introduced as complementary, air quality monitoring stations. These sensors are, however, not reliable due to the lower accuracy, short life cycle and corresponding calibration issues. Recent studies have shown that low-cost sensors are affected by relative humidity and temperature. In this paper, we explore methods to additionally improve the calibration algorithms with the aim to increase the measurement accuracy considering the impact of temperature and humidity on the readings, by using machine learning. A detailed comparative analysis of linear regression, artificial neural network and random forest algorithms are presented, analyzing their performance on the measurements of CO, NO
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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.