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Area of Science:

  • Environmental Science
  • Geospatial Analysis
  • Data Science

Background:

  • Mobile methane (CH4) sensors offer a promising method for detecting natural gas (NG) leaks in urban distribution networks.
  • Proprietary software for processing mobile CH4 survey data often functions as a "black box," limiting transparency and customization.
  • Effective analysis of large datasets from mobile CH4 surveys is crucial for identifying and quantifying NG leaks.

Purpose of the Study:

  • To present a detailed, step-by-step algorithm for generating natural gas leak indications from mobile CH4 sensor data.
  • To provide transparency into the data analysis process, including choices, challenges, and evolutionary insights.
  • To characterize the distribution of leak sizes within urban natural gas distribution systems based on mobile survey data.

Main Methods:

  • Development and application of a novel, transparent algorithm for processing mobile CH4 sensor data.
  • Systematic data analysis of surveys conducted across 15 diverse urban environments.
  • Quantification of leak indications and estimation of leak sizes using the developed algorithm.

Main Results:

  • The algorithm successfully generated over 6100 distinct natural gas leak indications.
  • Estimates of leak sizes were provided for identified leaks, enabling further characterization.
  • The study successfully characterized the distribution of leak sizes within the surveyed NG distribution systems.

Conclusions:

  • The described algorithm provides a transparent and effective method for analyzing mobile CH4 survey data to identify NG leaks.
  • Mobile surveys are a vital tool for managing natural gas distribution systems, with potential for enhanced value through technological and software advancements.
  • Data-driven insights derived from mobile CH4 surveys are essential for understanding and mitigating natural gas leaks in urban areas.