Skip to main content
ARS Home » Midwest Area » Bowling Green, Kentucky » Food Animal Environmental Systems Research » Research » Publications at this Location » Publication #363642

Research Project: Developing Safe, Efficient and Environmentally Sound Management Practices for the Use of Animal Manure

Location: Food Animal Environmental Systems Research

Title: Aerosol precursors from agricultural emissions

item Silva, Philip - Phil

Submitted to: American Association for Aerosol Research
Publication Type: Abstract Only
Publication Acceptance Date: 6/14/2019
Publication Date: 10/18/2019
Citation: Silva, P.J. 2019. Aerosol precursors from agricultural emissions. American Association for Aerosol Research. Paper No. 13AC.4.

Interpretive Summary:

Technical Abstract: Aerosol influences on the environment from agriculture are usually considered to be dominated by coarse mode dust with some secondary component in the fine mode from the impact of ammonia emissions. However, agricultural emissions include many reactive volatile organic compounds that potentially contribute to secondary aerosols as well. Emissions inventories are more uncertain regarding area sources like agriculture compared to mobile and point sources. In a number of locations, measured ammonia is either significantly higher or lower than modeling would predict and most reactive organic compounds are not adequately accounted for. Is this because different management practices contribute emissions different than specified inventory values? Are there missing sources in emissions inventories? We have performed field sampling experiments at poultry, dairy, and a hog farms to find answers to these questions. We will show evidence from field work that ammonia and volatile organic compounds react close to source to produce aerosol and discuss potential formation mechanisms. Data suggest that agricultural emissions may be underestimated as a source for new particulate matter in rural areas and that emissions inventories and air quality models may be missing important data.