scholarly journals Review of M.D. Lewan’s: Comments on “Ideas and perspectives: is shale gas a major driver of recent increase in global atmospheric methane?” by Robert W. Howarth (2019)

2020 ◽  
Author(s):  
Anonymous
Keyword(s):  
Shale Gas ◽  
Atmospheric Methane ◽  
Recent Increase

scholarly journals Comment on Ideas and perspectives: is shale gas a major driver of recent increase in global atmospheric methane? by Robert W. Howarth (2019)

2020 ◽  
Author(s):  
Michael D. Lewan
Keyword(s):  
Carbon Isotopes ◽  
Shale Gas ◽  
Stable Carbon Isotopes ◽  
Methane Emissions ◽  
Atmospheric Methane ◽  
Stable Carbon ◽  
Data Set ◽  
Recent Increase

Abstract. The ideas and perspectives presented by Howarth (2019) on shale gas being a major cause of recent increases in global atmospheric methane are based on his notion that stable carbon isotopes of methane (δ13C1) of shale gas are lighter than that of conventional gas based on a meager and unrepresentative data set. A plethora of publicly available data show that the δ13C1 values of shale gas are typically heavier than those of conventional gas. This contradiction renders his ideas, perspectives, and calculations on methane emissions from shale gas invalid.

scholarly journals Biogenic link to the recent increase in atmospheric methane over India

2021 ◽  
Vol 289 ◽  
pp. 112526
Author(s):  
A. Singh ◽  
J. Kuttippurath ◽  
K. Abbhishek ◽  
N. Mallick ◽  
S. Raj ◽  
...  
Keyword(s):  
Atmospheric Methane ◽  
Recent Increase

scholarly journals Model simulations of atmospheric methane and their evaluation using AGAGE/NOAA surface- and IAGOS-CARIBIC aircraft observations, 1997–2014

2018 ◽  
Author(s):  
Peter H. Zimmermann ◽  
Carl A. M. Brenninkmeijer ◽  
Andrea Pozzer ◽  
Patrick Jöckel ◽  
Andreas Zahn ◽  
...  
Keyword(s):  
Shale Gas ◽  
Atmospheric Chemistry ◽  
General Circulation ◽  
A Priori ◽  
Circulation Model ◽  
Gas Production ◽  
Coefficient Of Determination ◽  
Atmospheric Methane ◽  
Synoptic Variability ◽  
Methane Distribution

Abstract. The global budget and trends of atmospheric methane (CH4) have been simulated with the EMAC atmospheric chemistry – general circulation model for the period 1997 through 2014. Observations from AGAGE and NOAA surface stations and intercontinental CARIBIC flights indicate a transient period of declining methane increase during 1997 through 1999, followed by seven years of stagnation and a sudden resumed increase after 2006. Starting the simulation with a global methane distribution, scaled to match the station measurements in January 1997 and using inter-annually constant CH4 sources from eleven categories together with photochemical and soil sinks, the model reproduces the observations during the transient and constant period from 1997 through 2006 in magnitude as well as seasonal and synoptic variability. The atmospheric CH4 calculations in our model setup are linearly dependent on the source strengths, allowing source segregated simulation of eleven biogenic and fossil emission categories (tagging), with the aim to analyze global observations and derive the source specific CH4 steady state lifetimes. Moreover, tagging enables a-posteriori rescaling of individual emissions with proportional effects on the corresponding inventories and offers a method to approximate the station measurements in terms of lowest RMS. Enhancing the a priori biogenic tropical wetland emissions by ~ 29 Tg/y, compensated by a reduction of anthropogenic fossil CH4 emissions, the all-station mean dry air mole fraction of 1792 nmol/mol could be simulated within a RMS of 0.37 %. The coefficient of determination R2 = 0.87 indicates good agreement with observed variability and the calculated 2000–2005 average interhemispheric methane difference between selected NH and SH stations of 119 nmol/mol matches the observations. The CH4 samples from 95 intercontinental CARIBIC flights for the period 1997–2006 are also accurately simulated by the model, with a 2000–2006 average CH4 mixing ratio of 1786 nmol/mol, and 65 % of the measured variability being captured. This includes tropospheric and stratospheric data. To explain the growth of CH4 from 2007 through 2013 in term of sources, an emission increase of 28.3 Tg/y CH4 is needed. We explore the contributions of two potential causes, one representing natural emissions from wetlands in the tropics and the other anthropogenic shale gas production emissions in North America. A 62.6 % tropical wetland contribution and of 37.4 % by shale gas emissions optimally fit the trend, and simulates CH4 from 2007–2013 with an RMS of 7.1 nmol/mol (0.39 %). The coefficient of determination of R2 = 0.91 indicates even higher significance than before 2006. The 4287 samples collected during 232 CARIBIC flights after 2007 are simulated with an RMS of 1.3 % and R2 = 0.8, indicating that the model reproduces the seasonal and synoptic variability of CH4 in the upper troposphere and lower stratosphere.

Methane emissions from shale gas production sites in Sichuan, China

2020 ◽  
Author(s):  
Ming Xue ◽  
Yi-wei Zhao ◽  
Jun-xin Fan ◽  
Dong-dong Cao
Keyword(s):  
Shale Gas ◽  
Infrared Camera ◽  
High Sensitivity ◽  
Distribution Patterns ◽  
Methane Emissions ◽  
Gas Production ◽  
Detection Methods ◽  
Background Concentration ◽  
Atmospheric Methane ◽  
Flowback Water

<p>We performed ground-based methane emission measurements (downwind OTM33A method, on site methane measurements) from shale gas production sites in Sichuan province, China (Changning-Weiyuan region, 18 facilities with 81 wells). The mountainous geological location of the sites, and the limited road access has guaranteed only 2 to 3 downwind OTM-33A measurements. A backpack type high-sensitivity methane analyzer was applied to identify methane emissions and map the atmospheric methane distribution inside the gas facilities. The results showed that: the methane level along the fence line of the 16 facilities kept stable at background concentration, with the other 2 enhanced by less than 2 ppm. Inside the 10 facilities putting into production after 2016, pneumatic controllers from the three-phase separator showed no emission since they were running on electricity. Flowback water tanks were the major methane sources with concentration around 354 to 500ppm. Occasionally, the loose venting outlet of the actuator had leakage with methane concentration around 45 ppm. The application of high-sensitivity methane analyzer inside the facility has provided more detailed emission characteristics which could not be found by infrared camera before. This study could provide insights to the emission behavior from components and the distribution patterns of methane inside the shale gas facilities in Sichuan, China. For sites with a non-favored conditions for downwind measurements, other detection methods such as drone-based might be better to implement.</p>

scholarly journals Ideas and perspectives: is shale gas a major driver of recent increase in global atmospheric methane?

2019 ◽  
Vol 16 (15) ◽  
pp. 3033-3046 ◽  
Author(s):  
Robert W. Howarth
Keyword(s):  
Natural Gas ◽  
Shale Gas ◽  
Fossil Fuels ◽  
Global Climate ◽  
Gas Production ◽  
Carbon Stable Isotope ◽  
Atmospheric Methane ◽  
Late 20Th Century ◽  
The Past ◽  
Biogenic Source

Abstract. Methane has been rising rapidly in the atmosphere over the past decade, contributing to global climate change. Unlike the late 20th century when the rise in atmospheric methane was accompanied by an enrichment in the heavier carbon stable isotope (13C) of methane, methane in recent years has become more depleted in 13C. This depletion has been widely interpreted as indicating a primarily biogenic source for the increased methane. Here we show that part of the change may instead be associated with emissions from shale-gas and shale-oil development. Previous studies have not explicitly considered shale gas, even though most of the increase in natural gas production globally over the past decade is from shale gas. The methane in shale gas is somewhat depleted in 13C relative to conventional natural gas. Correcting earlier analyses for this difference, we conclude that shale-gas production in North America over the past decade may have contributed more than half of all of the increased emissions from fossil fuels globally and approximately one-third of the total increased emissions from all sources globally over the past decade.

scholarly journals Quantifying atmospheric methane emissions from the Haynesville, Fayetteville, and northeastern Marcellus shale gas production regions

2015 ◽  
Vol 120 (5) ◽  
pp. 2119-2139 ◽  
Author(s):  
J. Peischl ◽  
T. B. Ryerson ◽  
K. C. Aikin ◽  
J. A. Gouw ◽  
J. B. Gilman ◽  
...  
Keyword(s):  
Shale Gas ◽  
Marcellus Shale ◽  
Methane Emissions ◽  
Gas Production ◽  
Atmospheric Methane

scholarly journals Using global isotopic data to constrain the role of shale gas production in recent increases in atmospheric methane

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Alexei V. Milkov ◽  
Stefan Schwietzke ◽  
Grant Allen ◽  
Owen A. Sherwood ◽  
Giuseppe Etiope
Keyword(s):  
Shale Gas ◽  
Gas Production ◽  
Atmospheric Methane ◽  
Isotopic Data

scholarly journals Model simulations of atmospheric methane (1997–2016) and their evaluation using NOAA and AGAGE surface and IAGOS-CARIBIC aircraft observations

2020 ◽  
Vol 20 (9) ◽  
pp. 5787-5809
Author(s):  
Peter H. Zimmermann ◽  
Carl A. M. Brenninkmeijer ◽  
Andrea Pozzer ◽  
Patrick Jöckel ◽  
Franziska Winterstein ◽  
...  
Keyword(s):  
Shale Gas ◽  
General Circulation ◽  
Fossil Fuel ◽  
A Priori ◽  
Circulation Model ◽  
Optimization Procedure ◽  
Rice Cultivation ◽  
Coefficient Of Determination ◽  
Atmospheric Methane ◽  
Tropical Wetlands

Abstract. Methane (CH4) is an important greenhouse gas, and its atmospheric budget is determined by interacting sources and sinks in a dynamic global environment. Methane observations indicate that after almost a decade of stagnation, from 2006, a sudden and continuing global mixing ratio increase took place. We applied a general circulation model to simulate the global atmospheric budget, variability, and trends of methane for the period 1997–2016. Using interannually constant CH4 a priori emissions from 11 biogenic and fossil source categories, the model results are compared with observations from 17 Advanced Global Atmospheric Gases Experiment (AGAGE) and National Oceanic and Atmospheric Administration (NOAA) surface stations and intercontinental Civil Aircraft for the Regular observation of the atmosphere Based on an Instrumented Container (CARIBIC) flights, with > 4800 CH4 samples, gathered on > 320 flights in the upper troposphere and lowermost stratosphere. Based on a simple optimization procedure, methane emission categories have been scaled to reduce discrepancies with the observational data for the period 1997–2006. With this approach, the all-station mean dry air mole fraction of 1780 nmol mol−1 could be improved from an a priori root mean square deviation (RMSD) of 1.31 % to just 0.61 %, associated with a coefficient of determination (R2) of 0.79. The simulated a priori interhemispheric difference of 143.12 nmol mol−1 was improved to 131.28 nmol mol−1, which matched the observations quite well (130.82 nmol mol−1). Analogously, aircraft measurements were reproduced well, with a global RMSD of 1.1 % for the measurements before 2007, with even better results on a regional level (e.g., over India, with an RMSD of 0.98 % and R2=0.65). With regard to emission optimization, this implied a 30.2 Tg CH4 yr−1 reduction in predominantly fossil-fuel-related emissions and a 28.7 Tg CH4 yr−1 increase of biogenic sources. With the same methodology, the CH4 growth that started in 2007 and continued almost linearly through 2013 was investigated, exploring the contributions by four potential causes, namely biogenic emissions from tropical wetlands, from agriculture including ruminant animals, and from rice cultivation, and anthropogenic emissions (fossil fuel sources, e.g., shale gas fracking) in North America. The optimization procedure adopted in this work showed that an increase in emissions from shale gas (7.67 Tg yr−1), rice cultivation (7.15 Tg yr−1), and tropical wetlands (0.58 Tg yr−1) for the period 2006–2013 leads to an optimal agreement (i.e., lowest RMSD) between model results and observations.

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