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Related Concept Videos

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To achieve precise distance measurements, especially in surveying and construction, certain corrections must be applied to account for potential sources of error like the standardization errors, temperature variations, and slope adjustments.Standardization error emerges when measurement equipment undergoes changes, such as wear, repairs, or weather impacts. To address this, surveyors compare the equipment’s readings to a standard. This process identifies any deviation that might lead to...
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Developing Relative Humidity and Temperature Corrections for Low-Cost Sensors Using Machine Learning.

Ivan Vajs1,2, Dejan Drajic1,2,3, Nenad Gligoric3,4

  • 1Innovation Center, School of Electrical Engineering, University of Belgrade, Bulevar Kralja Aleksandra 73, 11120 Belgrade, Serbia.

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Summary

Machine learning improves low-cost air quality sensors, enhancing accuracy for pollutants like carbon monoxide (CO), nitrogen dioxide (NO2), and particulate matter (PM10). This makes them reliable complements to existing monitoring networks.

Keywords:
air pollution measurementsartificial neural networkcalibrationlow-cost sensorsmachine learningtemperature and relative humidity

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Area of Science:

  • Environmental Science
  • Sensor Technology
  • Data Science

Background:

  • Traditional air quality monitoring networks are accurate but costly and static.
  • Low-cost sensors offer a complementary solution but suffer from reliability issues.
  • Sensor performance is significantly impacted by environmental factors like temperature and humidity.

Purpose of the Study:

  • To enhance the accuracy of low-cost air quality sensors.
  • To investigate the impact of temperature and humidity on sensor readings.
  • To apply machine learning for improved sensor calibration algorithms.

Main Methods:

  • Comparative analysis of machine learning algorithms: linear regression, artificial neural networks, and random forest.
  • Calibration algorithm development to account for temperature and humidity.
  • Performance evaluation on measurements of carbon monoxide (CO), nitrogen dioxide (NO2), and PM10 particles.

Main Results:

  • Achieved R-squared values ranging from 0.93-0.97 for CO, 0.82-0.94 for NO2, and 0.73-0.89 for PM10.
  • Demonstrated significant improvement in measurement accuracy using machine learning calibration.
  • Results varied depending on the pollutant and the time of year.

Conclusions:

  • Machine learning effectively improves the accuracy of low-cost air quality sensors.
  • Calibrated low-cost sensors can serve as valuable complementary tools to reference stations.
  • Enhanced spatial and temporal resolution of air quality data is achievable.