Luận văn an investigation of seasonal variation in aerosol optical properties from ground based and satellite measurements over the region of tainan, taiwan

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 Prof Tang Huang Lin, Ph.D Dr 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 groundbased 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 (22.9997° N, 120.2270° E) 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 1.142 On the other hand, 2011 was shown with the lowest record of AOD (675 nm) at 0.225 In addition, months of March and April (spring) were both found with the highest peak of AOD (675 nm) at 0.943 and 1.142, respectively In contrast, June and July (summer) were the months recorded with accordingly lowest peak of AOD (675 nm) at 0.224 and 0.225 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 AERONET, MODIS AOD, Ångström Exponent, Precipitable Keywords Water, SDA, HYSPLIT Trajectory Number of Pages Date of Submission 59 20/11/2017 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.D 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 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 ix PART I INTRODUCTION 1.1 Research Background and Rationale 1.2 Objectives of the Study 1.3 Scope of the Study 1.4 Statement of the Problem 1.5 Limitations 1.6 Requirement PART II LITERATURE REVIEW 2.1 Air Pollution: Atmospheric Aerosols 2.2 AERONET– Ground-Based Measurement 10 MODIS – Satellite Imagery of Aerosol Optical Depth 14 2.3.1 NASA EOSDIS: Worldview Application 15 2.3.2 NAAPS- Aerosol Modelling 16 19 2.4 Air Mass Trajectory 20 2.4.1 Concept of Trajectory 20 2.4.2 Application of Trajectory 21 PART III DATA AND METHODOLGY 24 3.1 Data Collection 24 3.1.1 AERONET – Ground-based measurement of Aerosol Optical Depth (AOD) 24 iv c 3.1.2 MODIS – Satellite Imagery of Aerosol Optical Depth and Ångström Exponent 26 3.1.3 Navy Aerosol Analysis and Prediction System (NAAPS) – Aerosol Modelling 27 3.1.4 Hybrid Single Particle Lagrangian Integrated Trajectory Model (HYSPLIT) READY – Back Trajectory Modelling 27 3.1.5 Wind field 28 3.2 Methodology 28 PART IV RESULT AND DISCUSSION 32 4.1 Annual and Monthly Aerosol Optical Depth 32 4.2 Spectral Deconvolution Algorithm (SDA) 41 4.3 NAAPS Aerosol Modelling 44 4.4 HYPSLIT READY Backward Trajectory 48 PART V CONCLUSION 51 CITED REFERENCES 54 v c LIST OF FIGURES Figure Number of deaths by thousands attributed to combined household (HAP) and ambient air pollutant (AAP)……………………………………………………………8 Figure Distributed AERONET sites throughout the globe 11 Figure Chen-Kung_Univ AERONET site Version Level Aerosol Optical Depth at 340nm, 380nm, 440nm, 500nm, 675nm, 870nm and 1020nm from year 2009 12 Figure 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 Applications of air mass trajectories in different fields (Umesh K and Bablu K., 2014) 22 Figure (a) Chen-Kung_Univ AERONET site’s availability of Level daily average of AOD by year (b) Chen-Kung_Univ AERONET site’s relative contribution of day to total record (%) 25 Figure Flow chart of the methodology used in the study .31 Figure (a) Monthly AOD mean at 675nm obtained from year 2009, 2010, 2011, 2013, and 2014 at Chen-Kung AEROENET site .32 Figure (b) Yearly AOD mean at 675nm obtained from year 2009, 2010, 2011, 2013, and 2014 at Chen-Kung AEROENET site .33 Figure Ångström exponents at 440-870 nm vs AODs at 675 nm in year 2009, 2010, 2011, 2013 and 2014 respectively 34 vi c Figure 10.a Taiwan MODIS Merged Dark Target/Deep Blue AOD (Land and Ocean)from January, 2014 .36 Figure 10.b Taiwan MODIS Merged Dark Target/Deep Blue AOD (Land and Ocean) from April 2014 37 Figure 10.c Taiwan MODIS Merged Dark Target/Deep Blue AOD (Land and Ocean) from July, 2014 38 Figure 10.d Taiwan MODIS Merged Dark Target/Deep Blue AOD (Land and Ocean) from October, 2014 38 Figure 11.a Taiwan MODIS Deep Blue Ångström Exponent from January, 2014 .39 Figure 11.b Taiwan MODIS Deep Blue Ångström Exponent from April, 2014 39 Figure 11.c Taiwan MODIS Deep Blue Ångström Exponent from July, 2014 .40 Figure 11.d Taiwan MODIS Deep Blue Ångström Exponent from October, 2014 40 Figure 12 Monthly SDA retrievals from years 2009-2011 and 2013-2014 from ChenKung_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 Back Trajectory Model Parameters Selected 30 Table Yearly AOD (675 and 440nm), and AE statistics obtained from ChenKung_Univ AERONET site .36 Table Monthly SDA statistics from 2009, 2010,2011,2013 and 2014 at ChenKung_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 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, 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 c 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 c 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 c Figure 14-17 shows a panel comparison of surface aerosol concentration among South East Asia region The figures demonstrate that amongst the surface concentrations observed around the region of Taiwan, a relative strong signature of sulfate carries the largest scope followed by smoke, while dust can be hardly seen on months of April and July That is to say, health related risks within the study region in accordance with air pollution are majorly influenced by transported sulfate and smoke concentrations 4.4 HYPSLIT READY Backward Trajectory Figure 18 overlays were used to compare months of January, April, July and October on a trajectory map generated in subject of Tainan, Taiwan’s 24-hour air parcel movements In particular, air parcels from months of October and January are seen mainly influenced by Northern trade wind Whereas, April and January were seen flowing initially from Southern trade wind Identically, the generated wind map (Figure 19) also displayed an air parcel movement coming from South East Asia during the seasons of spring to fall Thereupon, it is easy to say that the observed aerosols observed during these periods are hence, primarily influence by transportations from East China Sea as well as South East Asia regions 48 c Figure 18 Air parcel trajectory map of Tainan, Taiwan from months of January, April, July and October in year 2014 49 c Figure 19 Seasonal Wind Map at 850 hPa 50 c PART V CONCLUSION AND RECOMMENDATION Conclusion The year (2009,2010,2011,2013 and 2014) records of aerosol optical depth (AOD) alongside its ångström exponent α (AE) and preciptable water (PW), obtained from the ground-based Aerosol Robotic Network (AERONET) data were observed on the subject of identifying the seasonal aerosol variations and characteristics of Tainan, Taiwan With attention to the yearly aerosol concentration, 2010 was found with the lowest AOD (675 nm) mean of 0.307 and the highest AOD (675nm) mean of 0.395 in 2009 Accordingly, 2014 was shown with the highest record of AOD (675 nm) at 1.142 while 2011 with the lowest record of AOD (675 nm) at 0.225 In addition, March and April (spring season) were found with the highest peak of AOD (675 nm) at 0.943 and 1.142, respectively In contrast, June and July (summer season) were the months recorded with relatively low AOD (675 nm) at 0.224 and 0.225 Given these points, as summer seasons experience precipitation more often than spring, it can be considered that the existence of moisture content sets an alteration of aerosol variance on each season In accordance with large scale of high valued ångström exponent occurrences, an implication of fine particulate matter along the study area is denoted Subsequently, as fine (mode) particulate matter were highlighted with approximately more than 90% of aerosol total mode mean from SDA, sulfate was identified as the dominant existing surface concentration displayed in the entire middle of the seasons’ observations As a 51 c result, the data indicates higher health risks due to the large quantity of fine particulate matter Notably, the monthly aerosol comparison between the observed years puts to point that wet deposition brings a significant influence on AOD distributions As matter of fact, a record of low AOD values and an increase of ångström exponent (AE) were found during the months with high moisture content (PW) Accordingly, the monthly aerosol classification shows that from months of May to July, precipitable water was in its highest peak This result displaced a great influence on AOD concentrations’ vision In effect, AOD can be noticed with down falls throughout months of which high moisture content is present In particular, it is constructive to conclude that the aerosol mode accumulated in the study area shows that a pile of fine particulate matter is existing through out the entire Taiwan On logical grounds, the paper also supports the view of wet deposition as an efficient process of aerosol removal Consequently, both the generated wind and back trajectory map showed similar results with aerosol loadings primarily influenced by transportations from East China Sea and South East Asia regions Through an integration of both satellite and ground-based measurements, an effective aerosol assessment report was conveniently and easily conducted 52 c Recommendation A detailed characterization of aerosols are crucial to categorize aerosol concentrations through ground-based measurements Though MODIS - AOD and NAAPS aerosol modelling both presented an effective aerosol loading, an exhibition of aerosol characterization through ground-based measurements such as AERONET can possibly support a comparison of observed aerosol loadings between ground-based and satellite measurements; and thus, reduce the uncertainties in satellite aerosol retrievals and modelling of its effects on the climate globally The research only focused on a single AERONET station, hence, the study lack an observational dataset of any variances in terms of spatial characteristics Accordingly, this area is highlighted to be obtained as it would provide an ample support on quantifying the magnitude of any alterations in spatial linkage of which aerosol convections might increase in concentration The function of temporal and spatial analysis includes an investigation of both local emissions and long-range transport Since, aerosol transportations across international boundaries (long-range transport) were put into focus, a consideration for local emissions should be also put into focus in order to achieve a better understanding of the study area’s temporal and spatial aspect As the data displays a finer particulate matter dominance within the study area, the government and policy makers are advised to implement a stricter yet effective air pollutant regulations and new informative tools in the hand of air quality managers, regulators, as well as public in protecting the breathable air each individual consumes 53 c REFERENCES A.N Alias, M.Z MatJafri, H.S Lim, N.M Saleh, S.H Chumiran and A Mohamad, 2014 Inferring Ångström Exponent and Aerosol Optical Depth from AERONET 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