東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Data-Driven Methods for Modeling Emi...

Pendergrass, Andrew Cole,

Linked to FindBook

Google Book

Amazon

博客來

Data-Driven Methods for Modeling Emissions and Atmospheric Composition /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Data-Driven Methods for Modeling Emissions and Atmospheric Composition // Andrew Cole Pendergrass.
Author:	Pendergrass, Andrew Cole,
Description:	1 electronic resource (195 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-12, Section: B.
Contained By:	Dissertations Abstracts International86-12B.
Subject:	Atmospheric chemistry. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31938182
ISBN:	9798280716957

Data-Driven Methods for Modeling Emissions and Atmospheric Composition /
Pendergrass, Andrew Cole,

Data-Driven Methods for Modeling Emissions and Atmospheric Composition /Andrew Cole Pendergrass. - 1 electronic resource (195 pages)

Source: Dissertations Abstracts International, Volume: 86-12, Section: B.

This dissertation investigates how large remote sensing datasets of atmospheric composition can be combined with traditional chemical transport models to advance understanding of pollutant budgets. Chemical transport models can simulate past and future pollutant burdens by representing atmospheric transport, reactive processes, and pollutant sources and sinks, but are subject to error in any of these components. Recent advances in chemical data assimilation and machine learning offer novel methods to combine the strengths of chemical transport models with information encoded in large measurement libraries from satellites and other instruments. I further develop and apply these methods in two core areas: fine particulate matter concentrations (focusing in East Asia) and pollutant emissions quantification (focusing on methane). Specific topics addressed in my dissertation include the following:Quantifying surface fine particulate matter in East Asia using machine learning (Chapters 1 and 2). Inhalation of outdoor fine particulate matter (PM2.5) is a major public health burden. Surface instruments allow PM2.5 monitoring but cannot cover all areas, so satellite-based aerosol optical depth (AOD) measurements can be used in combination with machine learning to estimate gap-free surface PM2.5. Here I developed and applied a machine learning model to produce daily, high resolution maps of PM2.5 in East Asia. Pendergrass et al. (2022) Atmos. Meas. Tech., Pendergrass et al. (2025) Atmos. Env.Interpreting fine particulate matter trends in South Korea, 2011-2022 (Chapter 3). Despite steady reductions in precursor emissions, winter PM2.5 in South Korea has shown fluctuating trends. Here I apply results from Chapters 1 and 2 along with surface data and remote sensing products to analyze the drivers of PM2.5 concentrations. Results suggest a growing role for secondary PM2.5 production due in part to rising oxidant concentrations, sulfate reductions in favor of nitrate, and changing nighttime PM2.5 formation pathways. Pendergrass et al. submitted to Geophys. Res. Lett.Developing a chemical data assimilation platform and applying it to global methane emissions (Chapters 4 and 5). Satellite observations of pollutant concentrations do not offer direct information on pollutant sources. Bayesian optimization can fuse observational data with emissions inventories and constrain emissions based on both. Here I develop an open-source chemical data assimilation toolkit called CHEEREIO which uses the localized ensemble transform Kalman filter (LETKF) algorithm and the GEOS-Chem chemical transport model to optimize emissions and concentrations. I then apply CHEEREIO to methane, with a focus on explaining causes of the 2020-2022 methane surge. I attribute the surge to emissions from the tropics and use a satellite inundation product to suggest that wetlands play a key role.Pendergrass et al. (2023) Geosci. Mod. Dev., Pendergrass et al. submitted to Atmos. Chem. Phys.

English

ISBN: 9798280716957Subjects--Topical Terms:

544140
Atmospheric chemistry.
Subjects--Index Terms:

Data assimilation

Data-Driven Methods for Modeling Emissions and Atmospheric Composition /
LDR:04410nmm a22004453i 4500 001 2433826
005 20250905091022.5
006 m o d
007 cr|nu||||||||
008 261011s2025 miu||||||m |||||||eng d
020 $a 9798280716957
035 $a (MiAaPQD)AAI31938182
035 $a AAI31938182
040 $a MiAaPQD $b eng $c MiAaPQD $e rda
100 1 $a Pendergrass, Andrew Cole, $e author. $0 (orcid)0000-0002-8210-4983 $3 3823591
245 1 0 $a Data-Driven Methods for Modeling Emissions and Atmospheric Composition / $c Andrew Cole Pendergrass.
264 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2025
300 $a 1 electronic resource (195 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 86-12, Section: B.
500 $a Advisors: Jacob, Daniel Committee members: Wofsy, Steven; Kuang, Zhiming.
502 $b Ph.D. $c Harvard University $d 2025.
520 $a This dissertation investigates how large remote sensing datasets of atmospheric composition can be combined with traditional chemical transport models to advance understanding of pollutant budgets. Chemical transport models can simulate past and future pollutant burdens by representing atmospheric transport, reactive processes, and pollutant sources and sinks, but are subject to error in any of these components. Recent advances in chemical data assimilation and machine learning offer novel methods to combine the strengths of chemical transport models with information encoded in large measurement libraries from satellites and other instruments. I further develop and apply these methods in two core areas: fine particulate matter concentrations (focusing in East Asia) and pollutant emissions quantification (focusing on methane). Specific topics addressed in my dissertation include the following:Quantifying surface fine particulate matter in East Asia using machine learning (Chapters 1 and 2). Inhalation of outdoor fine particulate matter (PM2.5) is a major public health burden. Surface instruments allow PM2.5 monitoring but cannot cover all areas, so satellite-based aerosol optical depth (AOD) measurements can be used in combination with machine learning to estimate gap-free surface PM2.5. Here I developed and applied a machine learning model to produce daily, high resolution maps of PM2.5 in East Asia. Pendergrass et al. (2022) Atmos. Meas. Tech., Pendergrass et al. (2025) Atmos. Env.Interpreting fine particulate matter trends in South Korea, 2011-2022 (Chapter 3). Despite steady reductions in precursor emissions, winter PM2.5 in South Korea has shown fluctuating trends. Here I apply results from Chapters 1 and 2 along with surface data and remote sensing products to analyze the drivers of PM2.5 concentrations. Results suggest a growing role for secondary PM2.5 production due in part to rising oxidant concentrations, sulfate reductions in favor of nitrate, and changing nighttime PM2.5 formation pathways. Pendergrass et al. submitted to Geophys. Res. Lett.Developing a chemical data assimilation platform and applying it to global methane emissions (Chapters 4 and 5). Satellite observations of pollutant concentrations do not offer direct information on pollutant sources. Bayesian optimization can fuse observational data with emissions inventories and constrain emissions based on both. Here I develop an open-source chemical data assimilation toolkit called CHEEREIO which uses the localized ensemble transform Kalman filter (LETKF) algorithm and the GEOS-Chem chemical transport model to optimize emissions and concentrations. I then apply CHEEREIO to methane, with a focus on explaining causes of the 2020-2022 methane surge. I attribute the surge to emissions from the tropics and use a satellite inundation product to suggest that wetlands play a key role.Pendergrass et al. (2023) Geosci. Mod. Dev., Pendergrass et al. submitted to Atmos. Chem. Phys.
546 $a English
590 $a School code: 0084
650 4 $a Atmospheric chemistry. $3 544140
650 4 $a Environmental engineering. $3 548583
650 4 $a Atmospheric sciences. $3 3168354
650 4 $a Remote sensing. $3 535394
653 $a Data assimilation
653 $a Emissions
653 $a Machine learning
653 $a Methane
653 $a PM2.5
690 $a 0371
690 $a 0775
690 $a 0725
690 $a 0799
710 2 $a Harvard University. $b Engineering and Applied Sciences - Engineering Sciences. $e degree granting institution. $3 3765774
720 1 $a Jacob, Daniel $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 86-12B.
790 $a 0084
791 $a Ph.D.
792 $a 2025
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31938182