ArticleSensors (Basel, Switzerland)2019
Using A Low-Cost Sensor Array and Machine Learning Techniques to Detect Complex Pollutant Mixtures and Identify Likely Sources.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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.
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Who cites it
7 citing papers in PubMed.
- Application of Machine Learning for Calibrating Gas Sensors for Methane Emissions Monitoring.Sensors (Basel, Switzerland) · 2023Article
- Deep learning framework for sensor array precision and accuracy enhancement.Scientific reports · 2023Article
- Methodology for Quantifying Volatile Compounds in a Liquid Mixture Using an Algorithm Combining B-Splines and Artificial Neural Networks to Process Responses of a Thermally Modulated Metal-Oxide Semiconductor Gas Sensor.Sensors (Basel, Switzerland) · 2022Article
- Optimizing Urban Air Pollution Detection Systems.Sensors (Basel, Switzerland) · 2022Article
- An Approximation for Metal-Oxide Sensor Calibration for Air Quality Monitoring Using Multivariable Statistical Analysis.Sensors (Basel, Switzerland) · 2021Article
- Stationary and portable multipollutant monitors for high-spatiotemporal-resolution air quality studies including online calibration.Atmospheric measurement techniques · 2021Article
- Using Gas-Phase Air Quality Sensors to Disentangle Potential Sources in a Los Angeles Neighborhood.Atmospheric environment (Oxford, England : 1994) · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
An array of low-cost sensors was assembled and tested in a chamber environment wherein several pollutant mixtures were generated. The four classes of sources that were simulated were mobile emissions, biomass burning, natural gas emissions, and gasoline vapors. A two-step regression and classification method was developed and applied to the sensor data from this array. We first applied regression models to estimate the concentrations of several compounds and then classification models trained to use those estimates to identify the presence of each of those sources. The regression models that were used included forms of multiple linear regression, random forests, Gaussian process regression, and neural networks. The regression models with human-interpretable outputs were investigated to understand the utility of each sensor signal. The classification models that were trained included logistic regression, random forests, support vector machines, and neural networks. The best combination of models was determined by maximizing the F
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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.