Abstract. A system for measuring the two-dimensional (2-D) spatial distribution of atmospheric CO 2 over complex industrial sites and urban areas on the order of 1 to 30 km 2 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 CO 2 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. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

Abstract. This work assesses the impact of uncertainties in atmospheric state knowledge on retrievals of carbon dioxide column amounts (XCO 2 ) from laser differential absorption spectroscopy (LAS) measurements. LAS estimates of XCO 2 columns are normally derived not only from differential absorption observations but also from measured or prior knowledge of atmospheric state that includes temperature, moisture, and pressure along the viewing path. In the case of global space-based monitoring systems, it is often difficult if not impossible to provide collocated in situ measurements of atmospheric state for all observations, so retrievals often rely on collocated remote-sensed data or values derived from numerical weather prediction (NWP) models to describe the atmospheric state. A radiative transfer-based simulation framework, combined with representative global upper-air observations and matched NWP profiles, was used to assess the impact of model differences on estimates of column CO 2 and O 2 concentrations. These analyses focus on characterizing these errors for LAS measurements of CO 2 in the 1.57-μm region and of O 2 in the 1.27-μm region. The results provide a set of signal-to-noise metrics that characterize the errors in retrieved values associated with uncertainties in atmospheric state and provide a method for selecting optimal differential absorption line pairs to minimize the impact of these noise terms.

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Laser absorption spectroscopy (LAS) has been used over the last several decades for the measurement of trace gasses in the atmosphere. For over a decade, LAS measurements from multiple sources and tens of retroreflectors have been combined with sparse-sample tomography methods to estimate the 2-D distribution of trace gas concentrations and underlying fluxes from point-like sources. In this work, we consider the ability of such a system to detect and estimate the position and rate of a single point leak which may arise as a failure mode for carbon dioxide storage. The leak is assumed to be at a constant rate giving rise to a plume with a concentration and distribution that depend on the wind velocity. We demonstrate the ability of our approach to detect a leak using numerical simulation and also present a preliminary measurement.

Abstract. The Greenhouse gas Laser Imaging Tomography Experiment (GreenLITE™) trace gas measurement system, jointly designed and developed by Atmospheric and Environmental Research, Inc. and Spectral Sensor Solutions LLC, provides high-precision, long-path measurements of atmospheric trace gases including CO2 and CH4 over extended (0.04–25 km2) areas of interest. In 2015, a prototype unit was deployed in Paris, France, to demonstrate its ability to provide continuous observations of CO2 concentrations along horizontal air segments and two-dimensional (2-D) maps of time-varying CO2 concentrations over a complex urban environment. Subsequently, these data have been adapted to create a physically consistent set of horizontal segment mean concentrations for (1) comparisons to highly accurate in situ point measurements obtained for coincident times within the Greater Paris area, (2) inter-comparisons with results from high spatial and temporal regional carbon cycle model data, and (3) potential assimilation of these data to constrain and inform regional carbon cycle modeling frameworks. To achieve these ends, the GreenLITE™ data are calibrated against precise in situ point measurements to reconcile constant systematic as well as slowly varying temporal differences that exist between in situ and GreenLITE™ measurements to provide unbiased comparisons, and the potential for long-term co-assimilation of both measurements into urban-scale emission models. While both the constant systematic biases and the slowly varying differences may have different impacts on the measurement accuracy and/or precisions, they are in part due to a number of potential common terms that include limitation in the instrument design, uncertainties in spectroscopy and imprecise knowledge of the atmospheric state. This work provides a brief overview of the system design and the current gas concentration retrieval and 2-D reconstruction approaches, a description of the bias-correction approach, the results as applied to data collected in Paris, France, and an analysis of the inter-comparison between collocated in situ measurements and GreenLITE™ observations.

Abstract. Improved technologies and approaches to reliably measure and quantify fugitive greenhouse gas emissions from oil sands operations are needed to accurately assess emissions and develop mitigation strategies that minimize the cost impact of future production. While several methods have been explored, the spatial and temporal heterogeneity of emissions from oil sand mines and tailings ponds suggests an ideal approach would continuously sample an area of interest with spatial and temporal resolution high enough to identify and apportion emissions to specific areas and locations within the measurement footprint. In this work we demonstrate a novel approach to estimating greenhouse gas emissions from oil sands tailings ponds and open-pit mines. The approach utilizes the GreenLITE™ gas concentration measurement system, which employs a laser-absorption-spectroscopy-based, open-path, integrated column measurement in conjunction with an inverse dispersion model to estimate methane (CH4) emission rates from an oil sands facility located in the Athabasca region of Alberta, Canada. The system was deployed for extended periods of time in the summer of 2019 and spring of 2020. CH4 emissions from a tailings pond were estimated to be 7.2 metric tons per day (t/d) for July–October 2019, and 5.1 t/d for March–July 2020. CH4 emissions from an open-pit mine were estimated to be 24.6 t/d for September–October 2019. Uncertainty in retrieved emission for the tailings pond in March–July 2020 is estimated to be 2.9 t/d. Descriptions of the measurement system, measurement campaigns, emission retrieval scheme, and emission results are provided.

GreenLITE™ is a ground-based laser absorption spectroscopy system capable of measuring and mapping CO2 concentrations over areas up to 25 km2. The system was deployed for COP21 as a demonstration and has now completed a year of CO2 measurements over the city of Paris, France. We will discuss lessons learned and relevant data from the year-long deployment. Recently, the system has demonstrated the same measurement capability for CH4, and results from preliminary testing are presented.

Exelis has recently developed a novel laser-based instrument to aid in the autonomous real-time monitoring and mapping of CO 2 concentration over a two-dimensional area. The Greenhouse gas Laser Imaging Tomography Experiment (GreenLITE) instrument uses two transceivers and a series of retroreflectors to continuously measure the differential transmission over a number of overlapping lines of sight or "chords", forming a plane. By inverting the differential transmission measurements along with locally measured temperature (T), pressure (P) and relative humidity (RH) the average concentration of CO 2 along each chord can be determined and, based on the overlap between chords, a 2D map of CO 2 concentration over the measurement plane can be estimated. The GreenLITE system was deployed to the Zero Emissions Research and Technology (ZERT) center in Bozeman, Montana, in Aug-Sept 2014, where more than 200 hours of data were collected over a wide range of environmental conditions, while utilizing a controlled release of CO 2 into a segmented underground pipe [1]. The system demonstrated the ability to identify persistent CO 2 sources at the test facility and showed strong correlation with an independent measurement using a LI-COR based system. Here we describe the measurement approach, instrument design, and results from the deployment to the ZERT site.