Development and Evaluation of an Urban Atmospheric Inverse Modeling Framework for the Washington, DC and Baltimore, MD metropolitan area: Initial Results from a Wintertime Case Study

Published

August 12, 2026

Author(s)

Miguel Cahuich-Lopez, Christopher Loughner, Fantine Ngan, Anna Karion, Lei Hu, Israel Lopez Coto, Kimberly Mueller, Julia Marrs, Arlyn Andrews, John Miller, Brian McDonald, Colin Harkins, Congmeng Lyu, Meng Li, Kevin Gurney, Mark Cohen, Howard Diamond, Ariel Stein, James Whetstone

Abstract

Accurate and up-to-date city-specific pollutant emissions data are crucial to assess the impact of sources and sinks on atmospheric composition and air quality. Here, a study on the development and evaluation of an urban atmospheric inverse modeling system for the Washington, DC, and Baltimore, MD, metropolitan area (DCBA) is presented to improve emissions quantification. The system components include the CarbonTracker-Lagrange (CT-L) inverse model; the HYSPLIT atmospheric transport model driven by high-resolution Weather Research and Forecasting (WRF) model simulations that ingest urban meteorological observations; tower-based observations of the National Institute of Standards and Technology (NIST) Northeast Corridor Urban Test Bed; biospheric flux estimates from the Vegetation Photosynthesis and Respiration Model (VPRM); and 1-km Vulcan and 4-km GRA2PES bottom-up emissions inventories utilized as priors. Synthetic and real (authentic ) data inversion frameworks were implemented to verify the capability of the inversion system to retrieve anthropogenic emissions in January 2019, employing inert trace gases as proof of concept. This study differs from previous research because it utilizes enhanced urban atmospheric transport modeling coupled with tailored analytical CT-L inversions. We found that the system significantly improved the quantification of anthropogenic fluxes in DCBA. When assimilating authentic atmospheric measurement data, the system consistently detected lower emission estimates than the two priors (0.355 MtC to 0.399 MtC), allowing for refining the estimations of both bottom-up inventories. System estimates are similar to previous top-down wintertime estimations in the DCBA, with monthly mean afternoon uncertainty reduction also comparable to other studies (34.3% to 31.0%). This work has important implications for monitoring and verifying air pollutant emissions from urban centers.

Citation

Journal of Geophysical Research-Atmospheres

Pub Type

Journals

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https://doi.org/10.1029/2025JD045557

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Keywords

Baysian model, urban pollution

Citation

Cahuich-Lopez, M. , Loughner, C. , Ngan, F. , Karion, A. , Hu, L. , Lopez Coto, I. , Mueller, K. , Marrs, J. , Andrews, A. , Miller, J. , McDonald, B. , Harkins, C. , Lyu, C. , Li, M. , Gurney, K. , Cohen, M. , Diamond, H. , Stein, A. and Whetstone, J. (2026), Development and Evaluation of an Urban Atmospheric Inverse Modeling Framework for the Washington, DC and Baltimore, MD metropolitan area: Initial Results from a Wintertime Case Study, Journal of Geophysical Research-Atmospheres, [online], https://doi.org/10.1029/2025JD045557, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=960520 (Accessed September 1, 2026)

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