Mapping the Spatial Distribution of Methane in Houston, Texas

Natural gas has the potential to reduce greenhouse gas and air pollutant emissions by substituting for higher-emitting fossil fuels such as coal and oil. However, potential reductions could be offset by leaks of methane, the primary constituent of natural gas and a potent greenhouse gas that also contributes to background levels of ozone pollution. The rate of leaks from urban infrastructure is one of the most uncertain components in estimating overall emission rates from the natural gas life cycle, and has not been quantified previously in the greater Houston region. This has motivated our research team at Rice to undertake a measurements and modeling campaign to quantify these rates.

Modeling efforts will first develop a spatial distribution of expected methane and ethane emissions from different sources for the Houston region. Ethane is considered because it is co-emitted with methane from natural gas sources but not from landfills, wastewater treatment plants, and natural sources of methane (e.g., wetlands). Natural gas processing facilities and petrochemical plants report their emissions to the US EPA, and the routes of major transmission pipelines are available in geographic information systems. Because detailed characteristics of local natural gas distribution infrastructure are unavailable, we will use data on neighborhood age, housing density, and other features to map the expected spatial distribution of methane emissions from the local gas distribution system in the Houston area.

The Community Multiscale Air Quality (CMAQ) model will then be used to simulate the expected distribution of methane and ethane in the Houston region. CMAQ does not currently simulate variations in methane levels. Thus, we will modify CMAQ to simulate methane explicitly, expanding the model's capabilities for future studies to better characterize this important greenhouse gas and precursor of ozone pollution. Comparisons of modeled and measured levels of the gases will help identify potential discrepancies between the emission inventory and actual emission rates.