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Using A Low-Cost Sensor Array and Machine Learning Techniques to Detect Complex Pollutant Mixtures and Identify

Jacob Thorson1, Ashley Collier-Oxandale2, Michael Hannigan3

  • 1Mechanical Engineering, University of Colorado Boulder, Boulder, CO 80309, USA. Jacob.Thorson@colorado.edu.

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This study developed a sensor array and a two-step method to identify air pollutant sources like vehicle emissions and biomass burning. The best model achieved a 0.72 F1 score, showing promise for low-cost air quality monitoring.

Keywords:
VOCsclassificationemissionslow-cost sensorsregressionsensor arrays

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Area of Science:

  • Environmental Science
  • Analytical Chemistry
  • Sensor Technology

Background:

  • Accurate identification of air pollutant sources is crucial for environmental monitoring and public health.
  • Existing methods for source identification can be costly and complex.
  • Development of low-cost, effective sensor systems is needed.

Purpose of the Study:

  • To develop and evaluate a low-cost sensor array for identifying different air pollutant sources.
  • To create a robust data analysis method combining regression and classification for source attribution.
  • To assess the performance of various machine learning models in this application.

Main Methods:

  • Assembled an array of low-cost sensors to detect pollutant mixtures.
  • Simulated four classes of emission sources: mobile, biomass burning, natural gas, and gasoline vapors.
  • Applied a two-step approach: regression models to estimate compound concentrations, followed by classification models to identify sources.
  • Investigated multiple linear regression, random forests, Gaussian process regression, and neural networks for regression and classification.

Main Results:

  • A combination of multiple linear regression (for concentration estimation) and random forest (for classification) yielded the best performance.
  • The optimal model achieved a maximum F1 score of 0.72 on test data.
  • Human-interpretable regression models were explored to understand sensor signal utility.

Conclusions:

  • The developed low-cost sensor array and two-step analysis method show significant potential for identifying air pollutant sources.
  • The hybrid regression-classification approach effectively utilizes sensor data for source attribution.
  • This technology offers a promising avenue for accessible and widespread air quality monitoring.