Quantifying methane point sources from fine-scale satellite observations of atmospheric methane plumes

被引:176
|
作者
Varon, Daniel J. [1 ,2 ]
Jacob, Daniel J. [1 ]
McKeever, Jason [2 ]
Jervis, Dylan [2 ]
Durak, Berke O. A. [2 ]
Xia, Yan [3 ]
Huang, Yi [3 ]
机构
[1] Harvard Univ, Sch Engn & Appl Sci, Cambridge, MA 02138 USA
[2] GHGSat Inc, Montreal, PQ H2W 1Y5, Canada
[3] McGill Univ, Dept Atmospher & Ocean Sci, Montreal, PQ H3A 0B9, Canada
关键词
IMAGING SPECTROSCOPY; EMISSIONS; PERFORMANCE; MISSION; SPACE; CH4; QUANTIFICATION; INSTRUMENT; OZONE; LAYER;
D O I
10.5194/amt-11-5673-2018
中图分类号
P4 [大气科学(气象学)];
学科分类号
0706 ; 070601 ;
摘要
Anthropogenic methane emissions originate from a large number of relatively small point sources. The planned GHGSat satellite fleet aims to quantify emissions from individual point sources by measuring methane column plumes over selected similar to 10 x 10 km(2) domains with <= 50 x 50 m(2) pixel resolution and 1 %-5 % measurement precision. Here we develop algorithms for retrieving point source rates from such measurements. We simulate a large ensemble of instantaneous methane column plumes at 50 x 50 m(2) pixel resolution for a range of atmospheric conditions using the Weather Research and Forecasting model (WRF) in large eddy simulation (LES) mode and adding instrument noise. We show that standard methods to infer source rates by Gaussian plume inversion or source pixel mass balance are prone to large errors because the turbulence cannot be properly parameterized on the small scale of instantaneous methane plumes. The integrated mass enhancement (IME) method, which relates total plume mass to source rate, and the cross-sectional flux method, which infers source rate from fluxes across plume transects, are better adapted to the problem. We show that the IME method with local measurements of the 10 m wind speed can infer source rates with an error of 0.07-0.17 th(-1) + 5 %-12 % depending on instrument precision (1 %-5 %). The cross-sectional flux method has slightly larger errors (0.07-0.26 th(-1) + 8 %-12 %) but a simpler physical basis. For comparison, point sources larger than 0.3 th(-1) contribute more than 75 % of methane emissions reported to the US Greenhouse Gas Reporting Program. Additional error applies if local wind speed measurements are not available and may dominate the overall error at low wind speeds. Low winds are beneficial for source detection but detrimental for source quantification.
引用
收藏
页码:5673 / 5686
页数:14
相关论文
共 50 条
  • [31] Detecting high-emitting methane sources in oil/gas fields using satellite observations
    Cusworth, Daniel H.
    Jacob, Daniel J.
    Sheng, Jian-Xiong
    Benmergui, Joshua
    Turner, Alexander J.
    Brandman, Jeremy
    White, Laurent
    Randles, Cynthia A.
    ATMOSPHERIC CHEMISTRY AND PHYSICS, 2018, 18 (23) : 16885 - 16896
  • [32] A Fine-Scale Mangrove Map of China Derived from 2-Meter Resolution Satellite Observations and Field Data
    Zhang, Tao
    Hu, Shanshan
    He, Yun
    You, Shucheng
    Yang, Xiaomei
    Gan, Yuhang
    Liu, Aixia
    ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION, 2021, 10 (02)
  • [33] Improving Methane Point Sources Detection Over Heterogeneous Land Surface for Satellite Hyperspectral Imagery
    Sun, Erchang
    Wang, Xianhua
    Wu, Shichao
    Ye, Hanhan
    Shi, Hailiang
    An, Yuan
    Li, Chao
    Jiang, Yun
    IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2025, 18 : 699 - 711
  • [34] Quantifying Time-Averaged Methane Emissions from Individual Coal Mine Vents with GHGSat-D Satellite Observations
    Varon, Daniel J.
    Jacob, Daniel J.
    Jervis, Dylan
    McKeever, Jason
    ENVIRONMENTAL SCIENCE & TECHNOLOGY, 2020, 54 (16) : 10246 - 10253
  • [35] Using a deep neural network to detect methane point sources and quantify emissions from PRISMA hyperspectral satellite images
    Joyce, Peter
    Villena, Cristina Ruiz
    Huang, Yahui
    Webb, Alex
    Gloor, Manuel
    Wagner, Fabien H.
    Chipperfield, Martyn P.
    Barrio Guillo, Rocio
    Wilson, Chris
    Boesch, Hartmut
    ATMOSPHERIC MEASUREMENT TECHNIQUES, 2023, 16 (10) : 2627 - 2640
  • [36] Stable isotopic signatures of methane from waste sources through atmospheric measurements
    Bakkaloglu, Semra
    Lowry, Dave
    Fisher, Rebecca E.
    Menoud, Malika
    Lanoiselle, Mathias
    Chen, Huilin
    Rockmann, Thomas
    Nisbet, Euan G.
    ATMOSPHERIC ENVIRONMENT, 2022, 276
  • [37] A method for quantifying freshwater discharge rates from satellite observations and Lagrangian numerical modeling of river plumes
    Osadchiev, Alexander
    ENVIRONMENTAL RESEARCH LETTERS, 2015, 10 (08):
  • [38] Carbon isotopic analysis of atmospheric methane in urban and suburban areas: Fossil and non-fossil methane from local sources
    Moriizumi, J
    Nagamine, K
    Iida, T
    Ikebe, Y
    ATMOSPHERIC ENVIRONMENT, 1998, 32 (17) : 2947 - 2955
  • [40] ATMOSPHERIC METHANE RESPONSE TO BIOGENIC SOURCES - RESULTS FROM A 3-D ATMOSPHERIC TRACER MODEL
    FUNG, I
    MATTHEWS, E
    LERNER, J
    ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY, 1987, 193 : 6 - GEOC