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Potential of Spaceborne Lidar Measurements of Carbon Dioxide and Methane Emissions from Strong Point Sources.

Authors :
Kiemle, Christoph
Ehret, Gerhard
Amediek, Axel
Fix, Andreas
Quatrevalet, Mathieu
Wirth, Martin
Source :
Remote Sensing; Nov2017, Vol. 9 Issue 11, p1137, 16p
Publication Year :
2017

Abstract

Emissions from strong point sources, primarily large power plants, are a major portion of the total CO<subscript>2</subscript> emissions. International climate agreements will increasingly require their independent monitoring. A satellite-based, double-pulse, direct detection Integrated Path Differential Absorption (IPDA) Lidar with the capability to actively target point sources has the potential to usefully complement the current and future GHG observing system. This initial study uses simple approaches to determine the required Lidar characteristics and the expected skill of spaceborne Lidar plume detection and emission quantification. A Gaussian plume model simulates the CO<subscript>2</subscript> or CH<subscript>4</subscript> distribution downstream of the sources. A Lidar simulator provides the instrument characteristics and dimensions required to retrieve the emission rates, assuming an ideal detector configuration. The Lidar sampling frequency, the footprint distance to the emitting source and the error of an individual measurement are of great importance. If wind speed and direction are known and environmental conditions are ideal, an IPDA Lidar on a 500-km orbit with 2 W average power in the 1.6 μm CO<subscript>2</subscript> absorption band, 500 Hz pulse repetition frequency, 50 m footprint at sea level and 0.7 m telescope diameter can be expected to measure CO<subscript>2</subscript> emission rates of 20 Mt/a with an average accuracy better than 3% up to a distance of 3 km away from the source. CH<subscript>4</subscript> point source emission rates can be quantified with comparable skill if they are larger than 10 kt/a, or if the Lidar pulse repetition frequency is augmented. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20724292
Volume :
9
Issue :
11
Database :
Complementary Index
Journal :
Remote Sensing
Publication Type :
Academic Journal
Accession number :
126524196
Full Text :
https://doi.org/10.3390/rs9111137