東華大學圖書館 |

Expanding the Capabilities of Atmospheric Chemistry Models and Datasets Using Machine Learning and Data-Driven Methods.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Expanding the Capabilities of Atmospheric Chemistry Models and Datasets Using Machine Learning and Data-Driven Methods./
作者:	Kelp, Makoto.
面頁冊數:	1 online resource (269 pages)
附註:	Source: Dissertations Abstracts International, Volume: 84-12, Section: B.
Contained By:	Dissertations Abstracts International84-12B.
標題:	Atmospheric chemistry. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30488562click for full text (PQDT)
ISBN:	9798379611101

Expanding the Capabilities of Atmospheric Chemistry Models and Datasets Using Machine Learning and Data-Driven Methods.
Kelp, Makoto.

Expanding the Capabilities of Atmospheric Chemistry Models and Datasets Using Machine Learning and Data-Driven Methods. - 1 online resource (269 pages)

Source: Dissertations Abstracts International, Volume: 84-12, Section: B.

Thesis (Ph.D.)--Harvard University, 2023.

Includes bibliographical references

This dissertation investigates how data-driven approaches applied to atmospheric chemistry models and air quality datasets can open avenues for new research perspectives. Atmospheric chemistry models are essential tools for studying the impacts of human activity on the atmosphere, as well as for predicting future changes in atmospheric composition. However, traditional atmospheric chemistry models are computationally expensive and have limitations in their ability to accurately represent the complexity of atmospheric processes. Recent advances in machine learning and data-driven methods have enabled me to expand the capabilities of atmospheric chemistry models. These novel approaches take advantage of underlying patterns in the observations that are not easily captured by traditional models, and they replace the model's computational bottleneck with a faster emulator. My Ph.D. research has also focused on issues of environmental justice. Specific questions addressed in my dissertation include the following:• Machine learning for chemical solvers (Chapters 1 and 2). The most computationally expensive component of an atmospheric chemistry model is the chemical solver. Can we replace this differential equation solver with a faster, machine-learned emulator? Are machine learning approaches viable for emulating and replacing components of a complex 3-D chemical transport model?• Optimal and equitable air pollution sensor networks (Chapters 3 and 4). The current US EPA's fine particulate matter (PM2.5) monitoring network was largely designed with 1980s and 1990s air pollution in mind. Can we design a data-driven algorithm for an updated PM2.5 sensor network that captures both air pollution concentrations and variability across the contiguous United States, given current drivers of air pollution? Furthermore, communities of color in the US are disproportionately exposed to higher levels of PM2.5 air pollution at all income levels. Can we design air pollution sensor networks that account for and address historical racial and income disparities?• Prescribed fires and rural environmental justice (Chapter 5). Given projections of increased fire activity in a warming climate, solutions are needed to help policymakers and stakeholders plan for present-day wildfires and pave the way toward strategies for future wildfires. Can prescribed fires be used as a tool to mitigate future wildfire smoke exposure? How do wildfires affect population-weighted PM2.5 smoke exposure in states and rural environmental justice communities in the western US, and can prescribed fires be implemented to decrease that exposure? • Chemical data assimilation of ozone (Chapter 6). Chemical data assimilation is a tool that uses a numerical model's ability to accurately propagate information on relatively short time scales to construct global distributions of chemical species based on assimilated observations. Does the assimilation of ozone satellite observations improve NASA's GEOS Composition Forecast modeling system's simulations of ozone? Can chemical data assimilation offer a consistent framework for the interpretation of ozone satellite observations?

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798379611101Subjects--Topical Terms:

544140
Atmospheric chemistry.
Subjects--Index Terms:

Data-driven methodsIndex Terms--Genre/Form:

542853
Electronic books.

Expanding the Capabilities of Atmospheric Chemistry Models and Datasets Using Machine Learning and Data-Driven Methods.
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