ReviewSensors (Basel, Switzerland)2021
Calibration Method for Particulate Matter Low-Cost Sensors Used in Ambient Air Quality Monitoring and Research.
Review 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 16 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
16 citing papers in PubMed.
- Visibility-Based Calibration of Low-Cost Particulate Matter Sensors: Laboratory Evaluation and Theoretical Analysis.Sensors (Basel, Switzerland) · 2025Article
- In-Field Performance Evaluation of an IoT Monitoring System for Fine Particulate Matter in Livestock Buildings.Sensors (Basel, Switzerland) · 2025Article
- Calibration of Integrated Low-Cost Environmental Sensors for Urban Air Temperature Based on Machine Learning.Sensors (Basel, Switzerland) · 2025Article
- Efficient field correction of low-cost particulate matter sensors using machine learning, mixed multiplicative/additive scaling and extended calibration inputs.Scientific reports · 2025Article
- Research on the Measurement of Particulate Matter Concentration in Diesel Vehicle Exhaust Using the Light Scattering Method.Sensors (Basel, Switzerland) · 2025Article
- Parsimonious Random-Forest-Based Land-Use Regression Model Using Particulate Matter Sensors in Berlin, Germany.Sensors (Basel, Switzerland) · 2024Article
- Particulate matter 1µm (PMData in brief · 2024Article
- Calibration Methods for Low-Cost Particulate Matter Sensors Considering Seasonal Variability.Sensors (Basel, Switzerland) · 2024Article
- Indication Variability of the Particulate Matter Sensors Dependent on Their Location.Sensors (Basel, Switzerland) · 2024Article
- Application of an Ultra-Low-Cost Passive Sampler for Light-Absorbing Carbon in Mongolia.Sensors (Basel, Switzerland) · 2023Article
- Smart Multi-Sensor Calibration of Low-Cost Particulate Matter Monitors.Sensors (Basel, Switzerland) · 2023Article
- A Novel Bike-Mounted Sensing Device with Cloud Connectivity for Dynamic Air-Quality Monitoring by Urban Cyclists.Sensors (Basel, Switzerland) · 2022Article
- Establishing A Sustainable Low-Cost Air Quality Monitoring Setup: A Survey of the State-of-the-Art.Sensors (Basel, Switzerland) · 2022Review
- Practical Particulate Matter Sensing and Accurate Calibration System Using Low-Cost Commercial Sensors.Sensors (Basel, Switzerland) · 2021Article
- The Simulation of In-Situ Groundwater Detector Response as a Means of Identifying Beta Emitting Radionuclides by Linear Regression Analysis.Sensors (Basel, Switzerland) · 2021Article
- Development of Drone-Mounted Multiple Sensing System with Advanced Mobility for In Situ Atmospheric Measurement: A Case Study Focusing on PMSensors (Basel, Switzerland) · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Over the last decade, manufacturers have come forth with cost-effective sensors for measuring ambient and indoor particulate matter concentration. What these sensors make up for in cost efficiency, they lack in reliability of the measured data due to their sensitivities to temperature and relative humidity. These weaknesses are especially evident when it comes to portable or mobile measurement setups. In recent years many studies have been conducted to assess the possibilities and limitations of these sensors, however mostly restricted to stationary measurements. This study reviews the published literature until 2020 on cost-effective sensors, summarizes the recommendations of experts in the field based on their experiences, and outlines the quantile-mapping methodology to calibrate low-cost sensors in mobile applications. Compared to the commonly used linear regression method, quantile mapping retains the spatial characteristics of the measurements, although a common correction factor cannot be determined. We conclude that quantile mapping can be a useful calibration methodology for mobile measurements given a well-elaborated measurement plan assures providing the necessary data.
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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.