Date of Award

1-1-2021

Language

English

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

College/School/Department

Department of Atmospheric and Environmental Sciences

Content Description

1 online resource (xxxvi, 290 pages) : color illustrations, color maps.

Dissertation/Thesis Chair

Fangqun Yu

Committee Members

James J Schwab, Christopher J Walcek, Liming Zhou

Keywords

ammonia, artificial intelligence, cloud condensation nuclei, global modeling, machine learning, new particle formation, Atmospheric nitrogen compounds, Atmospheric nucleation, Ammonia, Air, Atmospheric aerosols, Condensation (Meteorology)

Subject Categories

Artificial Intelligence and Robotics | Atmospheric Sciences | Other Chemistry

Abstract

Atmospheric ammonia has received recent attention due to (a) its increasing trend across various regions of the globe; (b) the associated direct and indirect (through PM2.5) effects on human health, the ecosystem, and climate; and (c) recent evidence of its role in significantly enhancing atmospheric new particle formation (NPF or nucleation) rates. The mechanisms behind nucleation in the atmosphere are not fully understood, although over the last decade there have been significant developments in our understanding. This dissertation aims at improving our understanding of atmospheric ammonia in the atmosphere, its spatiotemporal variability, its role in atmospheric new particle formation, and its resulting contribution to aerosol number concentrations, with focus on cloud condensation nuclei number (CCN) concentrations.

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