THAI NGUYEN UNIVERSITY UNIVERSITY OF AGRICULTURE AND FORESTRY ERIKA ROMERO PADLAN AN INVESTIGATION OF SEASONAL VARIATION IN AEROSOL OPTICAL PROPERTIES FROM GROUND-BASED AND SATELLITE MEASUREMENTS OVER THE REGION OF TAINAN, TAIWAN BACHELOR THESIS Study Mode: Full time Major: Environmental Science and Management Faculty: International Programs Office Batch: 2014- 2017 Thai Nguyen, 20/11/2017 i c DOCUMENTATION PAGE WITH ABSTRACT Thai Nguyen University of Agriculture and Forestry Degree Program Bachelor of Environmental Science and Management Student Name Erika Romero Padlan Student ID DTN1454290058 Thesis Title An Investigation of Seasonal Variation in Aerosol Optical Properties from Ground-Based and Satellite Measurements Over the Region of Tainan, Taiwan Supervisor (s) Assoc. Tang Huang Lin, Ph. Do Thi Ngoc Oanh Abstract: Atmospheric aerosols suspended in the air have a range of hardly a nanometer (less than the width of the smallest virus) to a several micrometers that makes them inhalable easily. Therefore, different health effects could be caused with respect to the particle sizes.
As a result, the air pollution (atmospheric aerosols) assessment drew great attention to the people. At present, the ground- based measurements and remote sensing observations are the general approaches for air quality monitoring. Since the atmospheric aerosols has diverse temporal variation, this study presents an integration of both ground-based (AERONET) and satellite measurements (MODIS AOD) with regards to the seasonal variation from a 5-year worth of data throughout Tainan, Taiwan (22. The collected data suggested various aerosol accumulations and dominance in each season with corresponds to precipitable water.
With attention to the yearly AOD, 2014 was shown with the highest record of AOD (675 nm) at 1. On the other hand, 2011 was shown with the lowest record of AOD (675 nm) at 0. In addition, months of March and April (spring) were both found with the highest peak of AOD (675 nm) at 0. In contrast, June and July (summer) were the months recorded with accordingly lowest peak of AOD (675 nm) at 0.
High density of larger Ångström exponent (fine mode particles more than 90% from its total mode derivation from Standard Deconvolution Algorithm (SDA) were detected all year round which indicates the fine particulate matter (PM) dominance in all seasons. The magnitude of AOD (aerosol loading) are found low in correspond with aerosol removal due to high moisture content in the months of May to July. Meanwhile, the air mass flows of back trajectory monthly map from HYSPLIT model suggested that aerosols observed within study area are primarily influenced by the transportations from East China Sea as well as South East Asia regions. Keywords AERONET, MODIS AOD, Ångström Exponent, Precipitable Water, SDA, HYSPLIT Trajectory Number of 59 Pages Date of 20/11/2017 Submission ii c ACKNOWLEDGEMENT First and foremost, I am entirely grateful to The Almighty God as well as my family (Nanay Rosie, Tatay Jing, Kuya Aba and Kuya Edward) and friends (PTAN, Anne, Kat, Mishel, Ken and Martina) for giving me the strength and provision that helped brought me in completion of this research.
I wish to express my sincerest thanks to my research professor, Assoc. Professor Tang Huang Lin, Ph. who is in spite of busy schedule still manages to supplement me the additional knowledge I ought for, along with the necessary facilities needed for my research at Center for Space and Remote Sensing (CSRSR) of National Central University (NCU). I am also entirely grateful to Dr.
Do Thi Ngoc Oanh who guided me thoroughly with passion in order to present my paper ideally to the public. I place on record, my deepest thanks to Advance Education Program (AEP) for I consider myself a lucky individual, given the chance to meet and be part of the Environmental Sensing Laboratory who gave me such wonderful lab mates including, Mr. Wei Hung Lien and Ms. Chang Yi-Ling who supported me with great patience throughout my research.
My salute goes to all the coding you’ve done with different software just to retrieve a 5 value point (thought they were 12, sorry 100x) that I didn’t event got to use in the end. I am really sorry guys. I’ll make sure to make your teas next time we meet, I promise. Great appreciation also goes to my family in Christ at NCU International Fellowship for the endless prayer and support, especially to Yu Tang Chien for helping me out a lot with my data despite her own busy schedules.
I would also like to include a special note of ― 謝謝我醜陋的朋友們‖ to Mayor, Mayora, Ate Shawie, Batang Hamog, Mr. Right, Walao Eh Mommyta, and 美国人 (solely educational purposes) for making me fat and keeping me sane as I probably could have just turned into a complete skinny psycho because of how stubborn my data are. Thai Nguyen, 25/09/2017 ERIKA ROMERO PADLAN iii c TABLE OF CONTENTS ACKNOWLEDGEMENT. iii LIST OF FIGURES.
vi LIST OF ABBREVIATIONS .1 Research Background and Rationale .2 Objectives of the Study .3 Scope of the Study .4 Statement of the Problem .1 Air Pollution: Atmospheric Aerosols .2 AERONET– Ground-Based Measurement. 3 MODIS – Satellite Imagery of Aerosol Optical Depth .1 NASA EOSDIS: Worldview Application .2 NAAPS- Aerosol Modelling .4 Air Mass Trajectory .1 Concept of Trajectory .2 Application of Trajectory. DATA AND METHODOLGY .1 AERONET – Ground-based measurement of Aerosol Optical Depth (AOD). MODIS – Satellite Imagery of Aerosol Optical Depth and Ångström Exponent .3 Navy Aerosol Analysis and Prediction System (NAAPS) – Aerosol Modelling .4 Hybrid Single Particle Lagrangian Integrated Trajectory Model (HYSPLIT) READY – Back Trajectory Modelling.
RESULT AND DISCUSSION .1 Annual and Monthly Aerosol Optical Depth .2 Spectral Deconvolution Algorithm (SDA).3 NAAPS Aerosol Modelling .4 HYPSLIT READY Backward Trajectory. 54 v c LIST OF FIGURES Figure 1 Number of deaths by thousands attributed to combined household (HAP) and ambient air pollutant (AAP)……………………………………………………………8 Figure 2 Distributed AERONET sites throughout the globe.11 Figure 3 Chen-Kung_Univ AERONET site Version 2 Level 2 Aerosol Optical Depth at 340nm, 380nm, 440nm, 500nm, 675nm, 870nm and 1020nm from year 2009.12 Figure 4 NAAPS 4-panel surface aerosol concentration model with total optical depth (Sulfate: Orange/Red; Dust: Green/Yellow; Smoke: Blue) at the upper left, sulfate at the upper right, dust at the lower left and smoke at the lower right corner at 00:00Z 15 August, 2017 in South East Asia .19 Figure 5 Applications of air mass trajectories in different fields (Umesh K.25 Figure 7 Flow chart of the methodology used in the study.31 Figure 8 (a) Monthly AOD mean at 675nm obtained from year 2009, 2010, 2011, 2013, and 2014 at Chen-Kung AEROENET site.32 Figure 8 (b) Yearly AOD mean at 675nm obtained from year 2009, 2010, 2011, 2013, and 2014 at Chen-Kung AEROENET site.33 Figure 9 Ångström exponents at 440-870 nm vs. AODs at 675 nm in year 2009, 2010, 2011, 2013 and 2014 respectively.a Taiwan MODIS Merged Dark Target/Deep Blue AOD (Land and Ocean)from January, 2014.b Taiwan MODIS Merged Dark Target/Deep Blue AOD (Land and Ocean) from April 2014.c Taiwan MODIS Merged Dark Target/Deep Blue AOD (Land and Ocean) from July, 2014.d Taiwan MODIS Merged Dark Target/Deep Blue AOD (Land and Ocean) from October, 2014.a Taiwan MODIS Deep Blue Ångström Exponent from January, 2014.b Taiwan MODIS Deep Blue Ångström Exponent from April, 2014.c Taiwan MODIS Deep Blue Ångström Exponent from July, 2014.d Taiwan MODIS Deep Blue Ångström Exponent from October, 2014.40 Figure 12 Monthly SDA retrievals from years 2009-2011 and 2013-2014 from Chen- Kung_Univ AERONET Site.41 Figure 13 Mean bar graph of total mode aerosol and PW in each month of 2009-2011 and 2013-2014.43 Figure 14 NAAPS 4-panel surface aerosol concentration model with total optical depth (Sulfate: Orange/Red; Dust: Green/Yellow; Smoke: Blue) at the upper left, sulfate at the upper right, dust at the lower left and smoke at the lower right corner at 00:00Z 13 January 2014 in South East Asia. 44 Figure 15 NAAPS 4-panel surface aerosol concentration model with total optical depth (Sulfate: Orange/Red; Dust: Green/Yellow; Smoke: Blue) at the upper left, vii c sulfate at the upper right, dust at the lower left and smoke at the lower right corner at 00:00Z 13 April 2014 in South East Asia .45 Figure 16 NAAPS 4-panel surface aerosol concentration model with total optical depth (Sulfate: Orange/Red; Dust: Green/Yellow; Smoke: Blue) at the upper left, sulfate at the upper right, dust at the lower left and smoke at the lower right corner at 00:00Z 13 July 2014 in South East Asia .46 Figure 17 NAAPS 4-panel surface aerosol concentration model with total optical depth (Sulfate: Orange/Red; Dust: Green/Yellow; Smoke: Blue) at the upper left, sulfate at the upper right, dust at the lower left and smoke at the lower right corner at 00:00Z 13 October 2014 in South East Asia.
47 Figure 18 Air parcel trajectory map of Tainan, Taiwan from months of January, April, July and October in year 2014.49 Figure 19 Seasonal Wind Map at 850 hPa .50 LIST OF TABLES Table 1 Back Trajectory Model Parameters Selected .30 Table 2 Yearly AOD (675 and 440nm), and AE statistics obtained from Chen- Kung_Univ AERONET site.36 Table 3 Monthly SDA statistics from 2009, 2010,2011,2013 and 2014 at Chen- Kung_Univ AERONET site .43 viii c LIST OF ABBREVIATIONS AOD Aerosol Optical Depth AOT Aerosol Optical Thickness AE Ångström Exponent PW Precipitable Water SDA Standard Deconvolution Algorithm NRL Naval Research Laboratory NAAPS NRL Aerosol Analysis and Prediction System ARL Air Resources Laboratory AERONET Aerosol Robotic Network MODIS Moderate Resolution Imaging Spectroradiometer READY Real-time Environmental Applications and Display HYSPLIT Hybrid Single-Particle Lagrangian Integrated Trajectory WHO World Health Organization USGS United States Geological Survey EOSDIS Earth Observing System Data and Information System DT Dark Target ix c DB Deep Blue GIBS Global Imagery Browse Services AGL Above Ground Level NOAA National Oceanic and Atmospheric Administration NASA National Aeronautics and Space Administration GDAS Global Data Assimilation System hPa hectopascals UTC Coordinated Universal Time τa aerosol optical depth x c PART I.1 Research Background and Rationale Over the past decades, research has provided ample support for an ever-present ten-fold of inhalable solid particles which are drifting through the air just above the deserts, oceans, forests, mountains, ice and in every ecosystem entwined. Known as aerosols, these particles float across from the Earth’s stratosphere down to the atmosphere and have a range of hardly a nanometer— smaller than the width of the smallest virus that makes it easily inhalable — to a several micrometers that is about the size of a single human hair (Adam Voiland, 2010). Aerosol properties are emitted from the surface of the Earth by means of both naturally (e., sea-salt, dust, biogenic, emissions), as well as an event of human activities (e., combustion of fossil fuels, land cover change and, etc. Thus, directly related with their sizes, particles have different health effects (Wilson R.
Assigned as one of the major contributors to a wide variety of health problems than any other pollutant worldwide, World Health Organization (WHO) estimated that particle pollution contributes to roughly 7 million premature deaths per year; establishing it as the leading cause of mortality worldwide. As particulate matter (PM) develops high concentration, it alters risky health issues in particular with decline in lung functions that can result to asthma and other respiratory issues (Nwafor et al. 1 c Alongside the thriving economic growth of USA, Western, Europe, Southeast and East Asia, regional emissions of air pollutants are rapidly increasing over the past decades (Kato and Akimoto, 1992; Streets et al. As a result, great interest has been invested on observing air quality.
Monitoring approaches such as ground-based and satellite measurements are taken as a methodical solution to the worsening rapid air ambient quality. Ground-based measurements such as AERONET (AErosol RObotic NETwork) that has been established by both NASA and PHOTONS (PHOtométrie pour le Traitement Opérationnel de Normalisation Satellitaire; Univ. of Lille 1, CNES, and CNRS-INSU) provides better time resolution and accuracy, however, its effectivity in terms of observation range is restricted by the given settled location of the instrument.