February 20, 2018

Rapid Method for Measuring Area Methane Emissions

Funding and additional support for this paper was provided by DCS
Data for this paper came from the NYC methane work supported by DCS
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Bryce F. Payne Jr., Gas Safety Inc., USA
Center for Energy, Environment and Sustainability,
Wake Forest University, USA
Robert Ackley, Gas Safety Inc., USA
Mark F. Arend,

Optical Remote Sensing Lab – NOAA CREST,
City College of New York, USA


Methane is an important greenhouse gas, but methane emissions are poorly understood, in large part due to limited atmospheric methane data on local scales. Local and regional scale methane emissions data are urgently needed to improve modeling of future climate change, and support energy plans and policies to minimize future climate impacts of socio-economically needed energy utilization. There have been numerous recent reports on local ground-level ambient air methane surveys that have provided more thorough data on methane sources in some urban areas. Such surveys generate substantial amounts of high quality ground-level methane concentration data, usually with reliable time and geo-referenced location data. We examined the potential usefulness of such data sets for generation of estimates of methane emissions for surveyed areas. Our efforts focused on development of a generally applicable, relatively simple mass-balance approach to estimate area methane emissions from mobile, ground level ambient air methane concentration and local weather data. The data examined were collected in Manhattan, New York, USA over 5 days in late 2012. Using the ratio of methane emissions (μg m-2s-1) to natural gas usage (μg m-2s-1), the
resulting methane emissions estimates for Manhattan were compared to 5 other cities (emissions reported by other investigators using other methods). The emissions estimates for Manhattan derived from ground-level mobile methane surveys were within the range of the estimates for the other cities. In addition, the emissions rates reported for the cities indicate natural gas should not be considered more climate-beneficial than other fossil fuels.


We developed a generally applicable, simple method for calculating methane emissions from distributed ground level ambient air methane concentration, weather and PBL data. Methane data for Manhattan showed concentrations consistently increased from upwind to downwind areas on the island. The method provided plausible consistent estimates of methane emissions in Lower Manhattan even though methane survey data were collected under different wind conditions and in nested or separate neighboring areas. Our simple calculation based on changes in methane concentrations, wind speed, height of the planetary boundary layer and observed relative differences between the northern and southern parts of the island generated an estimated methane emissions rate of 66 μg·m-2s-1 for the whole island, and plausible rates for 2 less densely populated areas in the region. We examined methane emissions as a function of NG usage density to compare the Manhattan emissions rate indicated by our method to rates reported for 5 other cities by other investigators using other methods. Our estimated emission rate for Manhattan was within the range of rates among the other 5 cities. The emissions estimates for the 6 cities indicated use of natural gas in lieu of other fossil fuels will not provide any climate change advantage, and will likely do more harm than good. We conclude the proposed simple mass balance methane emissions estimation method based on mobile CRDS data produced plausible results even with only opportunistic data sets. The method should be considerably more effective if wind, PBL, and methane data can be collected in data surveys designed for the purpose of estimating emissions. Further efforts to apply the method opportunistically with respect to other previously collected data sets could rapidly generate measurement-based methane emissions estimates for numerous other areas. More fully developed and applied the method could substantially contribute to meeting the need for methane emissions data, and should be adaptable to other trace gases for which similar mobile measurement capabilities are available.