Project Number: 22-019

Research

Title: Refining Ammonia emission using inverse modeling and satellite observations over Texas and the Gulf of Mexico and investigating its effect on fine particulate matter

Institution(s) Represented: University of Houston (PI: Yunsoo Choi)

Lead PI: Yunsoo Choi

AQRP Project Manager: Elena McDonald-Buller

TCEQ Project Liaison: Khalid Al-Wali

Awarded Amount: $131,366.00

Abstract

As ammonia (N H 3 ) emissions exacerbate air quality and contribute to the formation of inorganic fine particulate matter ( PM 2.5 ), they are detrimental to human health by contributing to cardiovascular disease, asthma, and respiratory dysfunction (Pui et al., 2014; Cheng & Wang-Li, 2019). Owing to the contribution of N H 3 concentrations to the formation of PM 2.5 and its nonlinear relationship to the formation of ammonium nitrate (Zhu et al., 2015), the contribution of N H 3 becomes even greater. It affects air quality and climate change by altering radiative forcing through aerosol formation (Hauglustaine et al., 2014), modifying the carbon flux (Pinder et al., 2013), modifying the phase of secondary inorganic aerosols (Yang et al., 2018), enhancing light absorption caused by organic aerosols (Huang et al., 2018), and heterogeneous ice nucleation (Kumar et al., 2018). N H 3 also plays a major role in the nitrogen cycle by altering the nitrogen-containing compounds, including nitrous oxide ( N 2 O) and nitrogen oxide (N O x ) (Xu Zhenying et al., 2019). Excessive amounts of N H 3 deposition can also harm sensitive ecosystems through soil acidification (Howard, 2011), loss of biodiversity (Carfrae et al., 2004), and eutrophication (Paerl et al., 2002). Although N H 3 emissions significantly affect air quality, climate change, and public health, insufficient measurements have complicated the investigation of its effects (Momeni et al., 2022). Furthermore, the scarcity of related observations has resulted in significant uncertainties in modeling N H 3 and designing regulatory control plans (Paulot et al., 2014). Scarcity of reliable information regarding the spatial and temporal distribution of emissions, emission factors, management practices, and farming plans (Zhu et al., 2013; Zhu et al., 2015) have also resulted in uncertainties in bottom-up N H 3 emissions inventories.
Inverse modeling approaches using observational data are a well-known method of constraining modeling predictions and refining emissions inventories. Observational data often used for inverse modeling techniques are those obtained from remote sensing, such as ammonia (N H 3 ) columns from the Cross-track Infrared Sounder (CrIS) instrument. Although remote sensing data have significantly contributed to our understanding of the spatial patterns of pollutant columns, they are often limited because of deficient spatial and temporal coverage and a significant level of uncertainty associated with measurements. As opposed to remote sensing data, chemical transport models (CTM) provide comprehensive data with a high spatiotemporal resolution of all species, our information from CTM, however, is also uncertain due to the numerical representation of chemical and physical processes in the atmosphere, as well as uncertainties in modeling inputs. As inverse modeling techniques help leverage strengths in modeling data and observations, they improve modeling predictions by accounting for uncertainties in both predictions and observational data.
With this grant, Grantee will conduct an inverse modeling study over the state of Texas and the Gulf of Mexico using Community Multiscale Air Quality (CMAQ) models with the implementation of N H 3 remote sensing data from CrIS for 2019. Grantee will analyze N H 3 emissions from mobile, area, and point sources and emissions from anthropogenic and biogenic sources. In the inverse modeling study, Grantee will use satellite observations to update N H 3 emissions to constrain associated emissions, which are highly uncertain owing to a lack of N H 3 observations, resulting in errors in bottom-up calculated emissions. We will employ the iterative Finite Difference Mass Balance (iFDMB) inverse modeling technique to revise N H 3 emissions with respect to CrIS observations. Since running the iFDMB is computationally expensive and requires numerous iterations, using a reduced complexity CMAQ model (RCCM) for simulations will reduce the burden of computations while maintaining the accuracy of predictions. After updating the emissions inventory, Grantee will investigate the effect of adjusting N H 3 emissions on atmospheric chemistry, including PM 2.5 concentrations, and PM 2.5 inorganic and organic constituents.

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Research

Research

FY 2022 - 2023

Project Number: 22-019