Real-Time Monitoring and Mapping of CO2 Concentrations Over Extended Urban Area: Mapping the Two-Dimensional Distribution of CO2 over Paris, France

This work describes an extension to the Greenhouse gas Laser Imaging Tomography Experiment (GreenLITE) retrieval and reconstruction algorithms designed to aid in the mapping of the 2-D distribution of CO₂ over extended urban areas, and provides preliminary results from an Oct-Dec 2015 deployment of the GreenLITE system in Paris, France. The ground-based GreenLITE system combines real-time differential Laser Absorption Spectroscopy (LAS) measurements with a lightweight web-based data acquisition and product generation system to provide autonomous 24/7 monitoring of CO₂ over extended open-air environments. The current GreenLITE system is comprised of two transceivers and a series of retro-reflectors that continuously measure the differential transmission over a user-defined set of intersecting line-of-site paths or “chords” that form the plane of interest. These observations are first combined with in situ surface measurements of temperature (T), pressure (P) and relative humidity (RH) to compute the integrated CO₂ mixing ratios based on an iterative radiative transfer based modeling approach. The retrieved CO₂ mixing ratios are then grouped into temporally spaces sample sets and employed in a sparse sample reconstruction method to provide a tomographic-like representation of the 2-D distribution of CO₂ over the field of interest. This reconstruction technique defines the field of interest as a two dimensional gradient and a set of idealized plumes whose integrated values best match the observations.

In this work, we describe a method for integrating surface meteorological observations with observed LAS measurements to compute the integrated column CO₂ concentrations which facilitate real-time, continuous monitoring of column concentrations along 1-5km path lengths. In addition, we illustrate how these retrieved CO₂ concentrations can be combined with a sparse sampling reconstruction algorithm to compute the distribution of CO₂ over an urban environment, which provides a 2-D view of the spatial and temporal distribution of CO₂. Finally, we present preliminary results from Oct-Dec 2015 deployment in a complex urban environment, and comparison to collocated in situ measurements and independent model data.