Abstract
A system for measuring the two-dimensional (2-D) spatial distribution of atmospheric CO2 over complex industrial sites and urban areas on the order of 1 to 30 km2 every few minutes with a spatial resolution as high as tens of meters has been developed and demonstrated over the past 3 years. The greenhouse gas (GHG) laser imaging tomography experiment (GreenLITE™) provides improved measurement capabilities for applications ranging from automated 24/7 monitoring of ground carbon storage/sequestration (GCS) sites to long-duration real-time analyses of GHG sources and sinks in urban environments. GreenLITE combines a set of sensors based on an intensity modulated continuous wave approach with 2-D sparse tomographic reconstruction mechanisms to compute a 2-D map of CO2 concentrations over the area of interest. GreenLITE systems have recently been deployed at a number of test facilities, including a 4000-h demonstration at a GCS site in Illinois and an urban deployment in Paris, France, from November 2015 to the present. This paper describes the GreenLITE concept and the associated measurement capabilities and provides proof of concept results and analyses of observations from both short-term tests as well as longer-term industrial and urban deployments.
Highlights
The GreenLITE system is designed to provide real-time quantitative measurements of near-surface atmospheric CO2 concentrations in a horizontal plane 1 to 50 m above ground level (AGL) over large (1 to >30 km2) areas with subelement resolution on the order of 100 to 200 m2. This approach provides a unique capability for a number of applications ranging from continuous remote monitoring of ground carbon storage/sequestration (GCS) facilities to the real-time measurement and assessment of subscale greenhouse gas (GHG) events within complex open air environments, e.g., industrial complexes and city sectors. This approach provides a unique perspective in contrast to the current state of the art for GCS atmospheric monitoring, which typically consists of LiDARs, in situ optical sensors, natural and introduced tracers, and eddy covariance (EC)[1] measurements
For all of the field tests/deployments except the farm test site—Zero Emissions Research and Technology (ZERT), Illinois Basin-Decatur Project (IBDP), Boulder, and Paris—comparisons could be made of the CO2 concentration values retrieved with the GreenLITE system to measurements from independent in situ instrumentation with varying degrees of calibration
Quantitative comparisons are made to show that (1) the GreenLITE system is measuring and computing realistic CO2 concentrations that are representative of the actual present CO2 levels and (2) the GreenLITE system is able to track local changes in CO2 concentrations over both short(minutes) and long- time scales
Summary
The GreenLITE system is designed to provide real-time quantitative measurements of near-surface atmospheric CO2 concentrations in a horizontal plane 1 to 50 m above ground level (AGL) over large (1 to >30 km2) areas with subelement resolution on the order of 100 to 200 m2. In situ sensors provide extremely precise measurements at a given point in space, making them ideal for monitoring and quantifying concentrations and emissions for well-defined point sources, e.g., stacks and well heads, as well as large diffuse source terms that are spatially homogeneous over extended areas They are limited in their ability to describe the dynamics of large spatially diverse areas of interest for GCS integrity monitoring or other large-scale applications, e.g., complex urban environments. Tower-based in situ instruments and other point measurements, along with static and dynamic inventories, are being used to help constrain models that provide dynamic estimates of changes in urban concentrations and fluxes.[2,10,11] Placing towers within the urban environments often presents difficulties due to siting challenges, and point measurements are often adversely impacted by nearby highly localized sources This susceptibility to local variations presents a significant challenge in assimilating these data into transport models with grid scales on the order of 1 to 4 km.
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