Urban GHG Emissions Measurement and Monitoring System (Urban-GEMMS)

A Prototype of the Integrated Urban U.S. Greenhouse Gas Measurement, Monitoring, and Information System

NOAA’s Air Resources Laboratory (ARL) is developing Urban-GEMMS, an operational capability to measure and model U.S. emissions of greenhouse gases (GHG), in collaboration with other OAR labs (Global Monitoring Laboratory and Chemical Sciences Laboratory) as well as the National Institute for Standards and Technology (NIST). Initially, this capability integrates existing, mature capabilities into an urban-scale operational GHG monitoring system covering the Washington DC-Baltimore region. Implementation of this operational system to other urban areas, and to larger scales (e.g., regional, national) is planned.

The primary goal of the urban-scale prototype is to reduce the lag between data collection and the availability of GHG emissions estimates. Current estimates are generally available on an annual basis. Creating a product on a more frequent basis enables and allows for more effective collaboration between regional and local city planners and federal agencies to monitor and evaluate their GHG mitigation policies. This prototype system is based on contemporaneous observations of GHG concentrations in the atmosphere; it complements and provides an independent quality control check on traditional emissions estimating methods.

Figure 1. Overall conceptual schematic of the Urban-GEMMS system. The different elements in this figure are described in more detail below. Credit: Maria Raykova (NOAA/Groundswell)

Monitoring greenhouse gas sources

This GHG Measurement, Monitoring, and Information System constitutes a portion of the US government’s initiative to address greenhouse gas emissions. The initial version of the prototype system being developed will target carbon dioxide (CO2), as it is the most significant GHG contributing to climate change, and approximately 70% of anthropogenic CO2 emissions are generated in urban areas. The next phase of the system will add methane (CH4), as it is also a large contributor to climate change and has significant urban emissions. This program will foster a better understanding of GHG emissions, which is essential to designing and evaluating efforts to reduce emissions. This system aims to not only improve the accuracy of emissions estimates, but also to provide more frequent insights into the results of efforts to reduce emissions. As GHG emissions mitigation policies are adopted, stakeholders will be able to monitor the results of their efforts.

Urban-GEMMS starts with atmospheric measurements of CO2 in the DC-Baltimore region and provides estimates of what emissions must have been to create the current CO2 concentrations. It is classified as a top-down emissions-estimating system, as it starts from observable concentrations of CO2 in the air. The process of using downwind observations to estimate emissions is used in a number of air pollution applications and is commonly called an inversion. The system is under active, collaborative development, and all aspects of the methodology described below are currently being developed, tested, and optimized.

 

The methodology

CO2 Observations

NIST’s Northeast Corridor GHG observation network provides fundamental input data to the prototype system. This network of tower-based carbon dioxide and methane observation stations was established in 2015 with the goal of quantifying GHG emissions in urban areas in the northeastern United States. A focus of the network is on Baltimore, MD, and Washington, DC, with a high density of stations in these two urban areas. Additional observing stations are available in the northeastern US to provide supplementary data on emissions throughout this complex region with high population density and multiple metropolitan areas.

Figure 2. Regional map of NIST Northeast Corridor tower locations. Map at right shows a detailed area near Washington, DC, and Baltimore, MD. Green triangles indicate regional sites, red triangles indicate urban sites, and blue triangles are rural or background sites surrounding the Washington–Baltimore region. Image Credit: NIST

Meteorological modeling

A fundamental component of Urban-GEMMS consists of high-resolution meteorological simulations (currently 1 km spatial resolution) that assimilate a variety of meteorological measurements. A key source of the meteorological measurements assimilated into the modeling are provided by ARL’s UrbanNet. This simulation is the result of a data reanalysis rather than a forecast. It is carried out after the fact, and uses meteorological measurements during the period of the simulation to create the best possible characterization of the atmosphere throughout that time period. The output from the meteorological modeling is a critical input to the HYSPLIT atmospheric transport and dispersion model for its calculations, as described below (see HYSPLIT footprints, below). 

Typically, GHG inversions are performed with the use of only afternoon CO2 observations. This is due to significant model biases and uncertainties in simulating the PBL during the nighttime hours and the morning and evening hours as the PBL is growing and shrinking, respectively. We aim to improve the model representation of the PBL by assimilating urban meteorological parameters into the WRF model and evaluate the simulations to demonstrate the efficacy of using all 24 hours of observations within GHG inversions.

The Weather Research and Forecasting (WRF) model is being utilized to produce meteorological fields for driving the HYSPLIT transport and dispersion model. Four-dimensional data assimilation (commonly known as “nudging”) is an established and effective technique in WRF to minimize model bias by integrating observational data during the simulation. The WRF model has been modified to accommodate observations with varying temporal frequencies and spatial radius of influence. Nudging effectively adjusts the predicted wind and temperature toward observations and further impacts dispersion simulations.

Figure 3. Time series of wind profile plots at the UrbanNet site located at the U.S. Department of Commerce Herbert C. Hoover Building (HCHB) on 16 Oct 2022 for non-nudged WRF and nudged WRF. The x axis is time (UTC), and the y axis represents the altitude (km AGL). Wind barbs are color-coded according to wind speed (knots). The observational nudging was carried out using the lidar and tower wind measurements taken from the rooftop of HCHB. The nudged WRF simulation produced stronger northerly wind components during 07-09 UTC and more accurately simulated a layer of stronger wind between 200 m and 800 m compared to the non-nudged simulation. Credit: NOAA/ARL

HYSPLIT footprints

The NOAA HYSPLIT atmospheric transport and dispersion model creates a series of “footprints,” to estimate the sensitivity of measured CO2 concentrations to upwind surface fluxes. The footprints are calculated by running the model backward in time from each CO2 measurement site for each CO2 measurement.  The footprint fields generated by HYSPLIT are one of the key inputs into the inversion model to estimate CO2 emissions. They help determine which emissions locations should be adjusted, and the magnitude of the adjustments, based on the observed concentrations of CO2.

Biogenic emissions

CO2 fluxes resulting from photosynthesis and respiration in natural ecosystems affect CO2 concentrations in the atmosphere. The measurements of CO2 in the region reflect the net result of anthropogenic and biogenic emissions. Thus, biological processes must be accounted for to estimate anthropogenic emissions. In Urban-GEMMS, the Vegetation Photosynthesis and Respiration Model (VPRM) is used to estimate biogenic sources and sinks within the modeling domain so they are accounted for in CO2 emissions estimates. VPRM estimates photosynthesis as a function of vegetation light-use efficiency, using satellite observations of surface greenness. Respiration is modeled as a nonlinear function of temperature, taking into account the impervious surface fraction in urban areas, as well as surface water content estimated from satellite data.

Figure 4. HYSPLIT generated footprints driven by the HRRR numerical weather prediction model output from one NIST tower in Arlington, VA, covering measurements taken during the afternoon hours (12-4pm EST) in January 2019. Higher footprint values reveal regions that are more susceptible to surface fluxes that impact the measurements at this location and time window. HYSPLIT simulations suggest that for those hours, Arlington, VA was more sensitive to CO2 fluxes from the south-southwest and northwest. The diamonds show the locations of all NIST towers. Credit: NOAA/ARL
Figure 5. Net Ecosystem Exchange (NEE, the sum of photosynthesis and ecosystem respiration) values over the Washington-Baltimore region from a VPRM simulation for August 2020. Negative values shown in green represent a net carbon uptake by vegetation. Positive values shown in red denote areas where carbon emissions from respiration outweigh carbon uptake (mostly in areas with lower vegetation density, such as urban areas and wetlands). Black triangles represent NIST tower locations. Credit: NIST

Background concentrations / boundary fluxes

CO2, influenced by upwind sources and sinks, enters into the modeling domain and contributes to the CO2 measurements within the domain. Therefore, the background fluxes of CO2 into the modeling domain of the inversion system must be accounted for in order to estimate emissions within the domain. These boundary fluxes create a background concentration that is subtracted from the urban- area CO2 measurements to isolate the urban-area emissions. Urban-GEMMS utilizes CO2 measurements at upwind stations to estimate these background fluxes. In addition to using the upwind station data to estimate background concentrations, alternative estimates of the background will be obtained by a blend of upwind station data with aircraft observations and/or simulated CO2 concentrations from regional, national, and global models.

Anthropogenic emissions prior estimates

Urban-GEMMS starts with a first guess (or a prior estimate) of the emissions in the region, and then uses the CO2 measurements to estimate how these emissions should be adjusted so that they are more consistent with downwind CO2 measurements. The first-guess emissions are based on the Greenhouse Gas and Air Pollutants Emissions System (GRA2PES) emissions inventory system developed by NOAA’s Chemical Sciences Laboratory (CSL) in collaboration with NIST.

GRA2PES is an hourly high-resolution (4km x 4km) data product with resolved vertical distribution for emissions of both greenhouse gases and air pollutants developed in a consistent framework. The dataset contains emissions over the contiguous United States covering major anthropogenic sectors, including energy, industrial fuel combustion and processes, commercial and residential combustion, oil and gas production, on-road and off-road transportation, etc (see Table below). Fossil fuel CO2 (ffCO2) emissions are developed along with those of air pollutants including CO, NH3, NOx, SO2, NMVOC, PM10 and PM2.5 with consistency in spatial and temporal distributions. An example of GRA2PES emissions data for the Urban-GEMMS domain for January 2019 is shown in Figure 7.

GRA2PES is a collaborative research project, aiming to strengthen the community’s ability to consistently model and map greenhouse gas and air pollutant emissions and associated uncertainties across the contiguous United States. GRA2PES builds off of several established inventories that have been extensively evaluated in air quality modeling studies and compared to satellite, aircraft and ground observations in multiple years.  The GRA2PES emissions data are uploaded to the National Institute of Standards and Technology (NIST) data center with a doi of https://doi.org/10.18434/mds2-3520. The aggregated, regridded, monthly high-resolution emissions for fossil fuel CO2, CO, NOx, SO2, and PM2.5 are interactively shown in the U.S. Greenhouse Gas Center.

Inversion using CarbonTracker-Lagrange

Once the above system components are assembled for a given month, they are used as inputs to the CarbonTracker-Lagrange (CT-L) inversion model developed by NOAA’s Global Monitoring Laboratory (GML). Conceptually, CT-L uses the time series of measured atmospheric concentrations of CO2 attributed to anthropogenic sources in the DC Baltimore region to estimate how much CO2 would have had to be emitted upwind of the measurement sites to produce the observed signals. The HYSPLIT footprints driven with the meteorological model fields provide the linkage between the emissions and observed concentrations required by the inversion technique. An overall map of the system is shown in Figure 6, and an example of the GRA2PES emissions inventory used as the first guess within the inversion modeling domain is shown in Figure 7.

Figure 6. Overall map of the system, showing CO2 and meteorological measurement sites, the domain of the high-resolution meteorological modeling domain, and the CarbonTracker-Lagrange domain. The DC and Baltimore beltways, and the portion of I-95 between the two cities, are shown (yellow roads) as they are typical routes for mobile GHG measurements. Image Credit: NOAA Air Resources Laboratory. Credit: NOAA/ARL
Figure 7. Overall system map including gridded CO2 emissions from the GRA2PES emissions inventory for Jan 2019 (mol CO2 km-2 hr-1), within the CarbonTracker-Lagrange modeling domain encompassing the Washington DC - Baltimore urban region. Image Credit: NOAA/ARL

Examples of top-down generated anthropogenic emissions estimates for afternoon hours during January 2019 using the Urban-GEMMS framework are shown in Figures 8 and 9. These emissions estimates used the GRA2PES anthropogenic emissions inventory as well as the 2015 Vulcan emissions inventory, which has a horizontal resolution of 1 km. Work is currently underway to increase the GRA2PES emissions inventory’s spatial resolution to 1 km.

Figure 8. Improved emissions estimates for the Washington, DC - Baltimore, MD metropolitan area for January 2019 were created with Urban-GEMMS using two bottom up anthropogenic emissions inventories as first-guesses (“priors”). Boxplots of the two bottom-up anthropogenic emissions inventories (Vulcan January 2015 emissions inventory and GRA2PES January 2019 emissions) and two top down inversion-corrected emissions estimates (posterior emissions) for January 2019 created from Urban-GEMMS using the two bottom-up emissions inventories as priors. Results are for the central portion of the computational domain, which is depicted by the black square in the map on the right. The diamonds on the map show the locations of the NIST towers where CO2 greenhouse gas measurements are made. On the boxplots, the bars indicate the 25th to 75th percentile range, whiskers depict 1.5 times the interquartile range (IQR), x’s show the outliers (>1.5 × IQR), the red line shows the median, the square markers the mean, and the dotted line the posterior mean. As can be seen, similar posterior emissions estimates were obtained for both priors. Credit: NOAA/ARL
Figure 9. Spatial plots showing (a) the Vulcan January 2015 emissions inventory used as a first guess (“prior”), (b) inversion-corrected (“posterior”) emissions estimates for January 2019 from Urban-GEMMS using Vulcan emissions as the prior, and (c) the emissions correction (posterior minus prior). Similar results are obtained from Urban-GEMMS that use the GRA2PES January 2019 emissions inventory as a prior and are shown in (d-f). Note that the Vulcan inventory has a spatial resolution of 1km and GRA2PES has a spatial resolution of 4km, hence the finer spatial details in (a-c) compared to (d-f). Work is currently underway to increase the spatial resolution of GRA2PES to 1km. Credit: NOAA/ARL

To date, the Urban-GEMMS framework has been used to estimate emissions during the afternoon hours. As stated above, we aim to demonstrate that high resolution NWP model simulations with assimilated urban meteorological observations can accurately simulate the urban atmosphere during all hours so the Urban-GEMMS system can use all 24 hours of measurement data, and hence be able to estimate emissions over all 24 hours in a given day.

Uncertainties

A fundamental component of Urban-GEMMS is estimates of uncertainties in the emissions estimates being generated. Stakeholders need to be aware of these uncertainties in order to better interpret the results of the system. Analysis of uncertainties in each of the system elements, and the entire system, will be continuously carried out in the operational system and included in the results provided by the system. Examples of different approaches that will be used to characterize uncertainties in the system include the following:

Comparisons against observations. Meteorological modeling results will be compared against observations in the region, e.g., from ARL’s UrbanNet. The system’s overall emissions estimates will be evaluated by comparison against mobile- and aircraft-based measurements periodically carried out in the region. To this end, NOAA carries out mobile GHG measurements with a specially outfitted vehicle, known as the Air Resources Car, throughout the Washington DC-Baltimore region. Flight-based GHG measurement campaigns are carried out in the region in collaborations between NOAA, NIST, the University of Maryland, and other institutions.

Tracers of Opportunity. Uncertainties will also be characterized and quantified by carrying out so-called Tracers of Opportunity tests, in which the HYSPLIT transport and dispersion model driven by high-resolution meteorological model output is ground-truthed using sources with known emissions (e.g., power plants with measured CO2 emissions) and downwind CO2 measurements. In these tests, the ability of the model to link the known emissions with the known downwind concentrations — a fundamental source of uncertainty in the inversion system — will be evaluated and quantified.

Ensembles. Uncertainties in the system’s emissions estimates will also be characterized and quantified by carefully chosen ensembles of estimates, based on variations in inputs and simulation procedures that reflect the uncertainties in various system components. The spread of the differently calculated emissions estimates can help characterize the uncertainty in the overall inversion system.

Post-processing, publishing and distribution of results

Urban-GEMMS is currently configured to generate monthly emissions estimates, estimates for a given month are anticipated to be available within two months. In addition to overall regional emission estimates for a given month, spatially and temporally resolved estimates for each month will also be provided for the region. It is understood that uncertainties will generally increase with estimates made on smaller and smaller temporal or spatial scales. The ultimate resolution(s) provided by the system will be determined based on a consideration of stakeholder needs as well as an analysis of relative uncertainties. Once the estimates are complete for a given month, they will be provided on a public-facing website, including trends and graphical, tabular, and other summaries of the results. More detailed data products will also be available for download. The goal is to make the outputs as useful and relevant to stakeholders as possible. Focus groups and other mechanisms will be used during the development of the system — and after the system becomes operational — to ensure that this goal is met.

End-to-end system

An end-to-end system is being developed and implemented to carry out all of the operations and estimates of the system in an automated manner. When fully implemented, the end-to-end system will carry out all of the operations outlined above. Parallel computations will be employed where possible — in cases where more than one element can be carried out at a time — and serial computations will be used for elements that require the results of previous computations. Specific input and output formats will be used so that a given element can be changed if desired and the new component can be incorporated into the system, in a “plug and play” manner. Throughout the end-to-end system, numerous quality assurance/quality control checks will be performed on all elements to ensure proper operation, and to recognize if something needs to be corrected. The system will be designed to be as robust as possible so that estimates can be made reliably and sustainably moving forward.