“Using Satellite and Sub-orbit Observations to Improve Boundary Layer Ammonia and PM2.5 Mixing Ratios associated with Agricultural Emission and Its Effects on Climate and Air Quality”

Principal Investigator: Dr. Viney P. Aneja
Co-Principal Investigators: Dr. Daniel Tong (NOAA/GMU), Dr. Jennifer Wei (Adnet Systems), Dr. Pius Lee (NOAA), Dr. Hang Lei (NOAA), Dr. Huisheng Bian, (NASA/JCET), Dr. Juying Warner (UMCP)
Ph.D. Students: Mr. William Battye, Ms. Casey Bray
National Aeronautics and Space Administration
September 1, 2015 – July 31, 2018
Funding: $105,000.00

Executive Summary

Concentrated animal feeding operations (CAFOs) are prominent sources of ammonia (NH3) and fine particulate matter (PM2.5) emissions and thus have a significant impact on air quality, human health, and climate. However, there are considerable uncertainties associated with NH3 and PM2.5 emissions from agriculture dominated regions, limiting our predictive capability to use atmospheric models to assess the impacts of agricultural activities on air quality and climate. Moreover, it remains elusive how atmospheric NH3 is changing and how such a change will affect air quality from local to regional scale.
We propose a 3-year multi-institutional integrated atmospheric composition and air quality project to utilize NASA Earth Science observations (satellite, sub-orbital and ground) to support air quality modeling and analysis of NH3 and PM2.5 change associated with intensive agriculture.
The primary objective of this study is to address three science questions: 1) How are agricultural emissions changing in the past decade? 2) How well can the 3-D chemical transport models be used to study the emissions, transformation and removal of NH3 and PM2.5 over agriculture-dominated regions? 3) How can NASA Earth observations be utilized to improve agricultural emissions, constraint model processes, and verify model prediction? To this end, we propose the following activities to be conducted by a team of emission scientists, Earth scientists and air quality modelers:

  • NASA data to constrain agricultural emissions: Apply NASA Earth observations and other datasets to improve agricultural NH3 and PM2.5 emissions using an inverse modeling approach similar to Gilliland et al. (2006). We will develop and test the proposed method to achieve this goal by conducting a rigorous analysis over eastern North Carolina, a region of intensive animal and crop agricultural operations, and then extend the study to Texas (beef cattle operations), the Mid-Atlantic, and the California Central Valley (dairy and beef cattle).
  • NASA data to examine the change in agricultural NH3: We propose to utilize the multi-year satellite observations of NH3 column density retrieved from AIRS-Aura to investigate the long-term change in NH3 composition in the atmosphere, and apply the temporal trend to adjust NH3 emission inventory to support air quality modeling.
  • Air quality prediction and analysis: We will integrate the new NH3 emission data into the NASA GMI modeling system and the NOAA CMAQ model to study the effect of agricultural emissions on air quality. We are also interested in utilizing these models to understand how the change in NH3 emissions will affect regional and global air quality; and climate forcing.
  • NASA data to verify model simulations: a suite of Earth Science data will be used to verify model results. These results will be compared and contrasted with existing NH3 and PM2.5 data collected from DISCOVER-AQ, AMoN, IMPROVE, and North Carolina Division of Air Quality networks.