Connecting observational & computational capabilities with model needs in atmospheric chemistry
Jessica Haskins, PhD


Understanding how humans impact the atmosphere & what to do next.
I use different climate models and all types of measurements of gas and particle composition in the atmosphere to understand the impacts of chemical reactions that: (1) control the lifetimes of greenhouse gases & (2) contribute to the formation of harmful air pollutants.
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My Research Themes: Oxidants, NOx & Heterogenous Chemistry
Oxidants control how long gases like methane stay in the atmosphere. When gases get oxidized they're more likely to form aerosols. Such heterogenous chemistry processes are hard to measure & poorly modeled. My research focuses on these topics to improve our understanding of how emissions are processed & removed from the atmosphere with climate and air pollution implications.
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Machine Learning & Data Science in Atmospheric Chemistry.
We have a lot of data. I want to use it. I'm interested in using modern computer science techniques to get the most out of our atmospheric data sets, and to make our climate models smarter. Parameterizing sub-grid-scale processes has never been more exciting.
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Why is research on aerosol formation important?

Fine particulate matter in the atmosphere, defined as particles with diameters below 2.5 μm (PM2.5) alter the radiative balance of the planet, degrade visibility, impact ecosystem health, and are a major public health concern. PM2.5 is implicated in health impacts from acute lower respiratory illness, cerebrovascular disease, ischemic heart disease, and chronic obstructive pulmonary disease to lung cancer (Burnett et al., 2014). Estimates of premature deaths per year worldwide attributable to outdoor air pollution driven by PM2.5, are greater than 3 million, with global chemistry-climate model predictions that a business-as-usual emission scenario could lead this to double by 2050 (Leieveld et al., 2015). Additionally, the impact of aerosols on radiative forcing are the leading uncertainty in climate forcings  (Myhre et al., 2013; Peace et al., 2020) underscoring the need to understand their various sources and how they may change over time. Thoroughly understanding sources of PM2.5, and correctly modeling its formation in global chemistry climate models is critical for designing effective air quality mitigation policies to reduce premature deaths related to air pollution and understand the total impact of future aerosol changes on climate.

Heterogenous Chemistry

gas-aerosol on description of jessica haskins research interests page

I’m broadly interested in heterogenous chemistry. Heterogenous chemistry connects gas phase anthropogenic emissions of pollutant precursors, like NOx, and natural emissions from the biosphere, like isoprene, to the formation of particulate matter in the aerosol phase.

Many heterogenous processes are mitigated by particle pH, aerosol surface area, availability of water vapor, concentrations of precursor species, and oxidants in the atmosphere. These processes are challenging to measure and therefore challenging to represent in models, and much remains to be discovered! My past research has focused on these themes as they applied to the role of chlorine in the troposphere, and my current research focuses on these themes as they apply to aerosol organic nitrate formation.

Connecting Models & Measurements

We live in a really exciting time right now where there are 20+ years of reliable field and satellite records and where computing power is constantly making leaps and bounds. My research interests involve continuing to connect the two, in a two way relationship, where results from each can inform the other. I was co-advised in graduate school between a measurement and modeling group. Now as a Postdoc, I am working with modelers and multiple measurement teams. I plan to continue to wedge myself in-between the two communities to consciously and fervently connect real world data to how we represent physical and chemical processes in models. I’m committed to doing this in a way that’s open source, accessible, and takes advantage of the data science and machine learning tools that are currently available. 

model-measurements on description of Jessica Haskins research interests page