Utilizing the GOCI Satellite to Estimate Hourly PM2.5 Concentrations in South Korea from 2015 – 2018 Pubblico

Abstract

Air quality in South Korea has been deteriorating in recent years, due to both domestic sources and long-range transport from China. Ground PM2.5 measurements are primarily located in urban environments, resulting in limited spatial and temporal coverage. To address this limitation, AOD retrieved from GOCI was utilized in multiple random forest machine learning models to estimate PM2.5 concentrations. We developed 8 separate random forest machine learning models with time-varying meteorology and spatially fixed land information parameters to be included in the model. A 6 km modeling grid with 6,307 pixels was used to match the parameters to the GOCI retrievals. The average GOCI AOD coverage in the study domain was 48%. The 10-fold cross validation R² ranged from 0.47 – 0.54 with RMSEs’ ranged between 9.74 – 13.34 µg/m³. The regression slope between observed and predicted hourly concentrations ranged between 1.2 – 1.3. Prediction maps of hourly PM2.5 levels indicate a higher concentration on the western coast of South Korea compared to the eastern coast. We further analyzed an episode of long-range transport from China to South Korea during March 10^th and 11^th, 2015. Results from those findings revealed evidence of long-range transport given that the western coast has higher concentrations. Overall, our hourly prediction models allow us to understand the spatiotemporal variations of PM2.5 concentrations in South Korea that could not have been done by ground stations alone.

Table of Contents

1.    Introduction.................................. 1

1.1 PM2.5 Background................... 1

1.2 South Korea’s Air Quality and Meteorology..................... 1

1.3 Ground Measurements of PM2.5 in South Korea........ 3

1.4 Air Pollution Studies in Korea Utilizing GOCI AOD....... 3

1.5 Study Objectives..................... 4

2.    Data and Methods......................... 6

2.1 Study Domain......................... 6

2.2 Dataset Description................ 6

2.2.1 GOCI Satellite YAER Version 2 Data............ 6

2.2.2 Ground PM2.5 Measurements............. 7

2.2.3. Meteorology Data......... 7

2.2.4. Ancillary Data.............. 8

2.2.5. Data Integration............ 9

2.3 Model Development............... 9

2.3.1 Random Forest Machine Learning Model........ 10

2.3.2 k-fold Cross Validation 11

3.    Results........................................ 13

3.1 Descriptive Statistics............ 13

3.2 Spatiotemporal AOD Coverage........................................ 14

3.3 Model Performance and Prediction........................ 16

3.4 Long Range Transport.......... 18

4.    Discussion and Conclusion........ 19

5.    References.................................. 22

6.    Tables and Figures...................... 27

About this Master's Thesis

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School	Rollins School of Public Health
Department	Environmental Health
Degree	M.S.P.H.
Submission	Master's Thesis
Language	English
Research Field	Environmental Health
Parola chiave	AOD GOCI PM2.5 South Korea
Committee Chair / Thesis Advisor	Yang Liu, PhD, Emory University

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