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The Global Positioning System (GPS) has become an indispensable tool in fieldwork, offering unparalleled precision and efficiency for surveying, navigation, and infrastructure development. By harnessing signals from a constellation of satellites, GPS receivers determine the location of objects with remarkable speed and accuracy, often completing calculations within a second.Advantages of Modern GPS TechnologyContemporary GPS receivers are designed to meet the practical demands of field...
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GPS surveying methods vary in application, accuracy, and data collection techniques, catering to diverse surveying and mapping needs. Static GPS, kinematic GPS, and real-time kinematic (RTK) surveying are widely used. Each technique offers distinct advantages.Static GPS involves placing one receiver at a known reference point and another at the target point. It collects exact positional data by observing multiple satellite ranges over an extended period, achieving centimeter-level accuracy for...
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Global Positioning System (GPS) technology has revolutionized navigation and positioning, but its accuracy is often compromised by various errors. These errors, stemming from environmental, satellite, and receiver-related factors, require careful mitigation to ensure reliable performance across applications.Atmospheric ErrorsGPS signals travel through the Earth’s ionosphere and troposphere, introducing delays which affect accuracy. The ionosphere is strongly influenced by charged particles,...
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The Global Positioning System (GPS) revolutionized positioning on Earth, providing precise location data through satellite ranging. The GPS system was developed in 1978 by the U.S. Department of Defense  for military use, and it became available for civilian applications in 1983, transforming fields including navigation, fleet management, and time synchronization for telecommunications systems.GPS consists of satellites in medium Earth orbit, about 20,200 kilometers above the surface,...
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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Developing microenvironment classification models for personal exposure assessment based on global positioning system

Jiwoong Yu1, Hyunwoo Jeon2, Kiyoung Lee2,3

  • 1Department of Public Health Science, Graduate School of Public Health, Seoul National University, Seoul, Republic of Korea.

Journal of Exposure Science & Environmental Epidemiology
|December 10, 2025
PubMed
Summary
This summary is machine-generated.

Accurate personal exposure assessment to air pollution is improved by using global positioning system (GPS) data. Machine learning models incorporating mobility patterns and GPS signal quality enhance microenvironment classification for better air pollution exposure estimates.

Keywords:
Deep learningGlobal positioning systemMachine learningMicroenvironment classificationPersonal exposure assessment

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Area of Science:

  • Environmental Epidemiology
  • Geospatial Data Analysis
  • Machine Learning Applications

Background:

  • Accurate personal exposure assessment to air pollution is critical for environmental epidemiology.
  • Traditional methods rely on time-activity diaries, which can be burdensome.
  • Differentiating indoor and outdoor pollutant concentrations poses a significant challenge.

Purpose of the Study:

  • To develop and evaluate microenvironment classification models using global positioning system (GPS) tracking data.
  • To enhance the accuracy of personal exposure assessment to air pollution.
  • To explore the utility of machine learning and deep learning for this task.

Main Methods:

  • Utilized data from the Korean Air pollutant EXposure (KAPEX) model project.
  • Developed classification models for Two-, Three-, and Four-level microenvironment categorizations.
  • Incorporated individual mobility patterns and GPS signal quality into machine learning (ML) and deep learning (DL) models.

Main Results:

  • Random forest achieved high accuracy (AUROC 0.963 for Two-level, 0.958 for Three-level).
  • Boosting performed best for Four-level categorization (AUROC 0.918).
  • Mobility patterns and GPS signal quality significantly improved classification accuracy.

Conclusions:

  • Integrating mobility patterns and GPS signal quality into ML models substantially enhances microenvironment classification accuracy.
  • This approach offers a more robust method for personal air pollution exposure assessment.
  • Machine learning models, particularly Random Forest and Boosting, show strong performance.