Inferring natural gas leak population characteristics using data from Google Street View cars

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  • Author: Zachary D. Weller, Jennifer A. Hoeting, and Joseph C. von Fischer
  • Date: 05 June 2019
  • Copyright: Image copyright of Patrick Rhodes

While new mobile monitoring technology has revolutionized our ability to measure pollutant levels over large regions, statistical methods for making inferences from data collected by these mobile systems are still being developed. In a paper published in Environmetrics, the authors introduce a new capture–recapture model to answer key inferential questions from data collected by mobile monitoring systems. We apply our new method to characterize populations of natural gas (NG) leaks in urban areas using data collected by atmospheric methane analyzers placed on Google Street View cars.

The paper is available via this link and the authors explain their findings in further detail below:

A calibration capture–recapture model for inferring natural gas leak population characteristics using data from Google Street View cars

Zachary D. Weller, Jennifer A. Hoeting, and Joseph C. von Fischer

Environmetrics, Volume 29, Issue 7, November 2018, e2519

https://doi.org/10.1002/env.2519|

thumbnail image: Inferring natural gas leak population characteristics using data from Google Street View cars

In this work, Weller, Hoeting, and von Fischer develop an innovative method for quantifying the leakiness of natural gas distribution systems in urban areas. Within cities, natural gas is delivered to homes through a network of pipes buried below roadways. Over time these pipes can develop leaks. Natural gas leaks are an economic loss and climate altering because natural gas is primarily composed of the potent greenhouse gas methane. Occasionally, natural gas leaks can also develop into an explosion hazard, compromising the safety of people and property. The Environmental Defense Fund has partnered with Google Earth Outreach to place high-sensitivity methane analyzers on Google Street View cars. These mobile surveys have enabled the collection of methane concentrations over large spatial regions and are used as a monitoring tool to detect and find natural gas leaks, but they do not detect every leak.

Using the data from these mobile surveys and statistical models typically used to estimate the size of animal populations, the authors of this paper estimate the total number of natural gas leaks within two urban areas, including those leaks that were not detected by the mobile survey. They also estimate the size of natural gas leaks by combining the mobile survey data with data collected during a controlled methane release experiment. This innovative work provides an understanding of the number of leaks that have gone undetected by the mobile surveys as well as characterizing the uncertainty of estimated leak size. The methods that Weller et al. developed can be used to assess the health of urban natural gas distribution systems and prioritize repairs of the largest natural gas leaks.

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