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Optimization of ambient Air Quality Monitoring Networks : (Part III).

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This study introduces two novel methods for designing multi-pollutant air quality monitoring networks (AQMN). These approaches, using index theory and Pareto optimality, optimize network configuration for simultaneous pollutant measurement.

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Area of Science:

  • Environmental Science
  • Environmental Engineering
  • Atmospheric Science

Background:

  • Existing methodologies focus on single-pollutant monitoring network design.
  • Practical air quality monitoring networks (AQMN) must address multiple pollutants simultaneously.
  • There is a need for robust methods to design multipollutant AQMN.

Purpose of the Study:

  • To develop novel approaches for designing multipollutant Air Quality Monitoring Networks (AQMN).
  • To enable simultaneous consideration of various pollutants in network design.
  • To provide practical methodologies for optimizing AQMN configuration.

Main Methods:

  • Development of two new methodologies for multipollutant AQMN design.
  • Method 1: Application of index theory.
  • Method 2: Utilization of Pareto optimality principles.

Main Results:

  • The study presents two distinct, viable approaches for multipollutant AQMN design.
  • Illustrative example using data from Taipei City, Taiwan, demonstrates the methodologies.
  • The proposed methods facilitate optimized network configuration for multiple pollutants.

Conclusions:

  • The developed methodologies offer effective solutions for designing comprehensive AQMN.
  • Index theory and Pareto optimality provide powerful frameworks for multipollutant network optimization.
  • These approaches are applicable to real-world air quality management scenarios.