Cargando…
Using A Low-Cost Sensor Array and Machine Learning Techniques to Detect Complex Pollutant Mixtures and Identify Likely Sources
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 classificati...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6749282/ https://www.ncbi.nlm.nih.gov/pubmed/31466288 http://dx.doi.org/10.3390/s19173723 |
_version_ | 1783452241610407936 |
---|---|
author | Thorson, Jacob Collier-Oxandale, Ashley Hannigan, Michael |
author_facet | Thorson, Jacob Collier-Oxandale, Ashley Hannigan, Michael |
author_sort | Thorson, Jacob |
collection | PubMed |
description | 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(1) score on ten-fold cross-validation data. The highest F(1) score, as calculated on testing data, was 0.72 and was produced by the combination of a multiple linear regression model utilizing the full array of sensors and a random forest classification model. |
format | Online Article Text |
id | pubmed-6749282 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-67492822019-09-27 Using A Low-Cost Sensor Array and Machine Learning Techniques to Detect Complex Pollutant Mixtures and Identify Likely Sources Thorson, Jacob Collier-Oxandale, Ashley Hannigan, Michael Sensors (Basel) Article 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(1) score on ten-fold cross-validation data. The highest F(1) score, as calculated on testing data, was 0.72 and was produced by the combination of a multiple linear regression model utilizing the full array of sensors and a random forest classification model. MDPI 2019-08-28 /pmc/articles/PMC6749282/ /pubmed/31466288 http://dx.doi.org/10.3390/s19173723 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Thorson, Jacob Collier-Oxandale, Ashley Hannigan, Michael Using A Low-Cost Sensor Array and Machine Learning Techniques to Detect Complex Pollutant Mixtures and Identify Likely Sources |
title | Using A Low-Cost Sensor Array and Machine Learning Techniques to Detect Complex Pollutant Mixtures and Identify Likely Sources |
title_full | Using A Low-Cost Sensor Array and Machine Learning Techniques to Detect Complex Pollutant Mixtures and Identify Likely Sources |
title_fullStr | Using A Low-Cost Sensor Array and Machine Learning Techniques to Detect Complex Pollutant Mixtures and Identify Likely Sources |
title_full_unstemmed | Using A Low-Cost Sensor Array and Machine Learning Techniques to Detect Complex Pollutant Mixtures and Identify Likely Sources |
title_short | Using A Low-Cost Sensor Array and Machine Learning Techniques to Detect Complex Pollutant Mixtures and Identify Likely Sources |
title_sort | using a low-cost sensor array and machine learning techniques to detect complex pollutant mixtures and identify likely sources |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6749282/ https://www.ncbi.nlm.nih.gov/pubmed/31466288 http://dx.doi.org/10.3390/s19173723 |
work_keys_str_mv | AT thorsonjacob usingalowcostsensorarrayandmachinelearningtechniquestodetectcomplexpollutantmixturesandidentifylikelysources AT collieroxandaleashley usingalowcostsensorarrayandmachinelearningtechniquestodetectcomplexpollutantmixturesandidentifylikelysources AT hanniganmichael usingalowcostsensorarrayandmachinelearningtechniquestodetectcomplexpollutantmixturesandidentifylikelysources |