Environmental remote sensing and systems analysis

CHANG ENVIRONMENTAL REMOTE SENSING and SYSTEMS ANALYSIS ENVIRONMENTAL ENVIRONMENT REMOTEREMOTE SENSINGSENSIN and and SYSTEMSSYSTEMS ANALYSISANALY EDITED BY NI-BIN EDITED CHANG BY NI-BIN CHAN Tai Lieu Chat Luong ENVIRONMENTAL REMOTE SENSING and SYSTEMS ANALYSIS EDITED BY NI-BIN CHANG Boca Raton London New York CRC Press is an imprint of the Taylor & Francis Group, an informa business MATLAB® is a trademark of The MathWorks, Inc and is used with permission The MathWorks does not warrant the accuracy of the text or exercises in this book This book’s use or discussion of MATLAB® software or related products does not constitute endorsement or sponsorship by The MathWorks of a particular pedagogical approach or particular use of the MATLAB® software CRC Press Taylor & Francis Group 6000 Broken Sound Parkway NW, Suite 300 Boca Raton, FL 33487-2742 © 2012 by Taylor & Francis Group, LLC CRC Press is an imprint of Taylor & Francis Group, an Informa business No claim to original U.S Government works Version Date: 20120123 International Standard Book Number-13: 978-1-4398-7744-9 (eBook - PDF) This book contains information obtained from authentic and highly regarded sources Reasonable efforts have been made to publish reliable data and information, but the author and publisher cannot assume responsibility for the validity of all materials or the consequences of their use The authors and publishers have attempted to trace the copyright holders of all material reproduced in this publication and apologize to copyright holders if permission to publish in this form has not been obtained If any copyright material has not been acknowledged please write and let us know so we may rectify in any future reprint Except as permitted under U.S Copyright Law, no part of this book may be reprinted, reproduced, transmitted, or utilized in any form by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying, microfilming, and recording, or in any information storage or retrieval system, without written permission from the publishers For permission to photocopy or use material electronically from this work, please access www.copyright.com (http://www.copyright.com/) or contact the Copyright Clearance Center, Inc (CCC), 222 Rosewood Drive, Danvers, MA 01923, 978-750-8400 CCC is a not-for-profit organization that provides licenses and registration for a variety of users For organizations that have been granted a photocopy license by the CCC, a separate system of payment has been arranged Trademark Notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation without intent to infringe Visit the Taylor & Francis Web site at http://www.taylorandfrancis.com and the CRC Press Web site at http://www.crcpress.com Contents Preface vii About the Editor .ix Contributors .xi Chapter Linkages between Environmental Remote Sensing and Systems Analysis Ni-Bin Chang Part I Water Quality Monitoring, Watershed Development, and Coastal Management Ni-Bin Chang and Zhemin Xuan Chapter Mapping Potential Annual Pollutant Loads in River Basins Using Remotely Sensed Imagery 35 Kazuo Oki, Bin He, and Taikan Oki Fahad A M Alawadi Chapter Remote Sensing to Predict Estuarine Water Salinity 85 Fugui Wang and Y Jun Xu Chapter Multitemporal Remote Sensing of Coastal Sediment Dynamics 109 Paul Elsner, Tom Spencer, Iris Möller, and Geoff Smith Chapter Estimating Total Phosphorus Impacts in a Coastal Bay with Remote Sensing Images and in Situ Measurements 123 Ni-Bin Chang and Kunal Nayee Chapter Monitoring and Mapping of Flood Plumes in the Great Barrier Reef Based on in Situ and Remote Sensing Observations 147 Michelle Devlin, Thomas Schroeder, Lachlan McKinna, Jon Brodie, Vittorio Brando, and Arnold Dekker iii iv Contents Part II Sensing and Monitoring for Land Use Patterns, Reclamation, and Degradation Chapter Satellite Remote Sensing for Landslide Prediction 191 Yang Hong, Zonghu Liao, Robert F Adler, and Chun Liu Chapter 10 Analysis of Impervious Surface and Suburban Form Using High Spatial Resolution Satellite Imagery 209 D Barry Hester, Stacy A C Nelson, Siamak Khorram, Halil I. Cakir, Heather M. Cheshire, and Ernst F Hain Chapter 11 Use of InSAR for Monitoring Land Subsidence in Mashhad Subbasin, Iran 231 Maryam Dehghani, Mohammad Javad Valadan Zoej, Mohammad Sharifikia, Iman Entezam, and Sassan Saatchi Chapter 12 Remote Sensing Assessment of Coastal Land Reclamation Impact in Dalian, China, Using High-Resolution SPOT Images and Support Vector Machine 249 Ni-Bin Chang, Min Han, Wei Yao, and Liang-Chien Chen Chapter 13 Mapping Impervious Surface Distribution with the Integration of Landsat TM and QuickBird Images in a Complex Urban– Rural Frontier in Brazil 277 Dengsheng Lu, Emilio Moran, Scott Hetrick, and Guiying Li Part III Air Quality Monitoring, Land Use/Land Cover Changes, and Environmental Health Concern Chapter 14 Using Lidar to Characterize Particles from Point and Diffuse Sources in an Agricultural Field 299 Michael D Wojcik, Randal S. Martin, and Jerry L Hatfield Chapter 15 Measurement of Aerosol Properties over Urban Environments from Satellite Remote Sensing 333 Min M Oo, Lakshimi Madhavan Bomidi, and Barry M Gross v Contents Chapter 16 DOAS Technique: Emission Measurements in Urban and Industrial Regions 377 Pasquale Avino and Maurizio Manigrasso Chapter 17 Interactions between Ultraviolet-B and Total Ozone Concentrations in the Continental United States 395 Zhiqiang Gao, Wei Gao, and Ni-Bin Chang Chapter 18 Remote Sensing of Asian Dust Storms 423 Tang-Huang Lin, Gin-Rong Liu, Si-Chee Tsay, N. Christina Hsu, and Shih-Jen Huang Chapter 19 Forest Fire and Air Quality Monitoring from Space 457 John J Qu and Xianjun Hao Chapter 20 Satellite Remote Sensing of Global Air Quality 479 Sundar A Christopher Preface In the last few decades, rapid urbanization and industrialization have altered the priority of environmental protection and restoration of air, soil, and water quality many times Yet it is recognized that the sustainable management of human society is necessary at all phases of impact from the interactions among energy, environment, ecology, public health, and socioeconomic paradigms The multidisciplinary nature of this concern for sustainability is truly a challenging task that requires employing a systems analysis approach Such a systems analysis approach links several disciplinary areas with each other to promote the concept of sustainable management Just as a sophisticated piece of music involves many different instruments played in unison, systems analysis requires a holistic viewpoint and a plethora of tools in sensing, monitoring, and modeling that have to be woven together to explore the state and function of air, water, and land resources at all levels With the aid of systems analysis, this comprehensive collection includes a variety of research work that results from years of experience and that reflects the contemporary advances of remote sensing technologies This unique publication presents and applies the most recent synergy of remote sensing technologies that will advance the overall understanding of the sensitivity of key environmental quality issues in relation to human perturbations These perturbations can be caused by collective or individual impacts of economic development and globalization, population growth and migration, and climate change on atmospheric, terrestrial, and aquatic environmental systems Specifically, this book aims to address the following intertwined research topics in the nexus of the environmental remote sensing and systems analysis: • What are the potential impacts on water quality when the management of the nitrogen cycle in a watershed changes, affecting ecosystem health in marine and fresh waters? • What are the regional impacts of an oil spill in coastal environments? • How will water quality in coastal bay and estuary regions be affected by changing salinity concentrations, turbidity levels, and sediment transport processes? • How will landslide and land subsidence in association with the changing hydrologic cycle influence human society? • How will the effects of urbanization affect the rate of water infiltration at urban–rural interfaces? • How can the impact of air pollution on meteorology, climatology, and public health be evaluated in association with the changing land use and land cover patterns from urban to global scales? The presentations in this book uniquely elaborate on the intrinsic links of the above questions that capture important interactions among three thematic areas vii viii Preface They include (1) water quality monitoring, watershed development, and coastal management; (2) sensing and monitoring for land use patterns, reclamation, and degradation; and (3) air quality monitoring, land use/land cover changes, and environmental health concerns On this foundation, many new techniques and methods developed for spaceborne, airborne, and ground-based measurements, mathematical modeling, and remote sensing image-processing tools may be realized across these three distinctive thematic areas This book will be a useful source of reference for undergraduate and graduate students and working professionals who are involved in the study of environmental science, environmental management, sustainability science, environmental informatics, and agricultural and forest sciences It will also benefit scientists in related research fields, as well as professors, policy makers, and the general public As the editor of this book, I wish to express my great appreciation for the contributions of many individuals who helped write, proofread, and review these book chapters I am indebted to the 58 authors and coauthors within the scientific community who have shared their expertise and contributed much time and effort in the preparation of this book I also wish to give credit to the numerous funding agencies promoting scientific research in environmental remote sensing that have led to the generation of invaluable findings presented here I acknowledge the management and editorial assistance of Irma Shagla and Kari Budyk Dr Ni-Bin Chang Director, Stormwater Management Academy University of Central Florida Orlando, Florida MATLAB® is a registered trademark of The MathWorks, Inc For product information, please contact: The MathWorks, Inc Apple Hill Drive Natick, MA 01760-2098 USA Tel: 508 647 7000 Fax: 508-647-7001 E-mail: info@mathworks.com Web: www.mathworks.com About the Editor Dr Ni-Bin Chang is currently a professor with the Civil, Environmental, and Construction Engineering Depart­ ment, University of Central Florida (UCF) He is also a senior member of the Institute of Electrical and Electronics Engineers (IEEE) affiliated with the IEEE Geoscience and Remote Sensing Society and the IEEE Computational Intel­ ligence Society He has earned the selectively awarded titles of Certificate of Leadership in Energy and Environment Design (LEED) in 2004, Board Certified Environmental Engineer (BCEE) in 2006, Diplomat of Water Resources Engineer (DWRE) in 2007, elected member (Academician) of the European Academy of Sciences (MEAS) in 2008, and elected Fellow of American Society of Civil Engineers (ASCE) in 2009 He was one of the founders of the International Society of Environmental Information Management and the former editor-in-chief of the Journal of Environmental Informatics He is currently an editor, associated editor, or editorial board member of 20+ international journals ix 20 Satellite Remote Sensing of Global Air Quality Sundar A Christopher CONTENTS 20.1 Introduction 479 20.2 Aerosols and Human Health 479 20.3 Monitoring Particulate Matter Pollution .480 20.3.1 Ground-Based Monitoring .480 20.3.2 Satellite Remote Sensing of Particular Matter Pollution 481 20.4 Application Potential 488 20.5 Conclusions 488 References 489 20.1  INTRODUCTION Urban air quality has become a critical public health concern in many parts of the globe with the increase of urbanization and industrialization during the last few decades Almost half of the world’s population now live in the urban areas, and their number will increase to billion by the end of this decade Particulate matter (PM) (or aerosols) and ozone are two of the major pollutants affecting the air quality in urban areas throughout the world PM is a complex mixture of solid and liquid particles that vary in size and composition and remain suspended in the air Many chemical, physical, and biological components of atmospheric aerosols are identified as being potentially harmful to respiratory and cardiopulmonary human health Aerosols have many sources from both natural and anthropogenic activities These include naturally occurring aerosols from windblown dust and episodic activities such as forest fires/agricultural burning (mostly anthropogenic) Increasing human factors such as combustion from automobiles and industries and emissions from power plants also contribute Apart from direct emissions, PM is also produced by other processes such as gas-to-particle conversion in the atmosphere 20.2  AEROSOLS AND HUMAN HEALTH Atmospheric aerosols are one of the most important components of the earthatmosphere system and play an important role in climate- and weather-related processes (Kaufman et al 2002) They vary in size from 0.001 to 100 µm Air pollution has both short- and long-term effects Short-term impacts include respiratory infections, 479 480 Environmental Remote Sensing and Systems Analysis headaches, nausea, allergic reactions, and irritation to the eyes, nose, and throat Shortterm air pollution can intensify the medical conditions of individuals with asthma and emphysema Long-term effects include lung cancer, heart disease, chronic respiratory disease, and even damage to the brain, nerves, liver, or kidneys Various studies have underscored the links between air pollution and human health (e.g., Dockery et al 1993; Hu et al 2009; Pope et al 2009; Wong et al 2008; Yang et al 2011) In 1952, London experienced one of the worst smog disasters, which killed more than 4000 people in a few days due to a very high concentration of PM (14 mg∙m–3) in the air PM with aerodynamic diameters less than 2.5 µm (PM2.5) can cause respiratory and lung disease and even premature death The World Health Organization (WHO) estimates that 4.6 million people die each year from causes directly attributable to air pollution A medical study (Pope 2000) concludes that fine particles and sulfur oxide–related pollution are associated with causes such as lung cancer and cardiopulmonary mortality The same study also states that an increase of 10 µg∙m–3 in fine particulates can cause approximately a 4%, 6%, and 8% increased risk of all cause, cardiopulmonary and lung cancer mortality, respectively Indirectly, air pollution significantly affects the economy by increasing medical expenditures and expenditures for preserving the surrounding environment Therefore monitoring PM air quality is critical 20.3  MONITORING PARTICULATE MATTER POLLUTION 20.3.1  Ground-Based Monitoring Various agencies around the world are using ground monitors for measuring air pollution For example, the United States Environmental Protection Agency (U.S EPA) monitors air quality by measuring PM and ozone concentration at thousands of ground-based monitoring stations across the country At the majority of the stations, PM2.5 (i.e., fine particle with a diameter of 2.5 µm or less) is measured using a tapered-element oscillating microbalance (TEOM) instrument A vibrating hollow tube called the tapered element is set in oscillation at resonant frequency and an electronic feedback system maintains the oscillation amplitude When the ambient air stream enters the mass sensor chamber and particulates are collected at the filter, the oscillation frequency of the tapered element changes, and the corresponding mass change is calculated as the change in measured frequency at time t to the initial frequency at time t0 The mass concentration is then calculated from dust mass, time, and flow rate Ideally, only the collection of aerosol mass on the filter should change the tapered element frequency However, temperature fluctuations, humidity changes, flow pulsation, and change in filter pressure could affect the TEOM performance Even in best-case scenarios when the operational parameters can be held constant, the heat-induced loss of volatile material could pose serious errors in the PM2.5 mass However, various correction factors are usually applied to adjust for these factors, although the PM2.5 mass usually represents the lower limits of a true value (Grover et al 2005) The ground monitors have several advantages The measurement techniques can be standardized and applied across all ground monitors They can measure pollution Satellite Remote Sensing of Global Air Quality 481 throughout the day and can also provide information They can measure pollution regardless of clouds since these are filter-based measurements that are usually located at the surface They also have some disadvantages The obvious one is that they are point measurements and are not representative of pollution over large spatial areas Costs for installing and maintaining such equipment must also be taken into account The U.S EPA issues National Ambient Air Quality Standards (NAAQS) for six criteria pollutants, namely, ozone, PM, carbon monoxide, sulfur dioxide, lead, and nitrogen oxides Standards for PM were first issued in 1971 and then revised in 1987 In September 2006, the U.S EPA revised its 1997 standards to tighten the criteria The 2006 standards reduced the 24-hour mean PM2.5 mass concentration standard from 65 to 35 µg∙m–3 and retained the current annual PM2.5 standard at 15 µg∙m–3 The EPA reports an Air Quality Index (AQI) based on the ratio between 24-hour averages of the measured dry particulate mass and NAAQS, and it can range from nearly zero in a very clean atmosphere to 500 in very hazy conditions (see Al Saadi et al 2005 for further details) In recent years, other countries in Europe and Australia, Japan, and China have also started monitoring PM2.5 mass as one measure of air quality conditions 20.3.2  Satellite Remote Sensing of Particular Matter Pollution Satellite data have tremendous potential for mapping the global distribution of aerosols and their properties (Chu et al 2003; Martin 2008) PM air pollution from spaceborne sensors is obtained by largely using passive remote sensing such as reflected solar radiation or emitted thermal radiation Hoff and Christopher (2009) provided a thorough review of the various satellite sensors available for studying PM2.5 In this chapter, we chose the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the National Aeronautics and Space Administration’s (NASA) Terra (morning satellite with equatorial crossing time of 10:30 am) and Aqua (afternoon satellite with equatorial crossing time of 1:30 pm) satellites that provide systematic retrieval of cloud and aerosol properties over land (King et al 1992) The MODIS has 36 channels, covers the ultraviolet to the thermal infrared part of the electromagnetic spectrum, and provides near daily global coverage due to its large swath width Its spatial resolution is from 250 to 1000 m Aerosol optical depth (AOD) is an important aerosol parameter retrieved from satellite observations, representing columnar loading of aerosols in the atmosphere along with the fraction of fine mode aerosol, which is an indicator of anthropogenic pollution Several studies conducted over land reveal that MODIS AOD retrievals are within expected uncertainty levels (Remer et al 2005) In the absence of clouds, the reflected solar radiation for an aerosol layer from the sun to the Earth atmosphere and back to the satellite is a function of surface reflectance, molecular scattering, and absorption The top of atmosphere reflectance is a function of sun-satellite viewing geometry and can be related to AOD, which is the columnar value of aerosol extinction (absorption plus scattering) For example, the seasonal distribution of MODIS AOD at 550 nm can be shown in Figure 20.1 In the Northern hemisphere spring and summer, pollution over Asia, Africa, and other parts of the globe is readily seen Dust is prevalent in Africa during the summer 482 Environmental Remote Sensing and Systems Analysis 30 60 90 120 150 JJA –150 –120 –90 –60 –30 30 60 90 120 150 0.0 0.05 0.10 0.15 0.2 0.3 0.4 –50–40–30–20–10 10 20 30 40 50 –50–40–30–20–10 10 20 30 40 50 –150 –120 –90 –60 –30 MAM –150 –120 –90 –60 –30 30 60 90 120 150 30 60 90 120 150 50 40 30 20 10 –10–20–30–40–50 –50–40–30–20–10 10 20 30 40 50 DJF –150 –120 –90 –60 –30 30 60 90 120 150 SON –150 –120 –90 –60 –30 0.5 0.6 50 40 30 20 10 –10–20–30–40–50 –50–40–30–20–10 10 20 30 40 50 –150 –120 –90 –60 –30 0.8 1.0 30 60 90 120 150 1.5 FIGURE 20.1  Seasonal distribution of MODIS mid-visible aerosol optical depth where DJF is December, January, February; MAM is March, April, May; JJA is June, July, August; and SON is September, October, November for 2006 months, and these dust aerosols can be transported several hundred miles to the Atlantic Ocean Biomass burning is observed from satellites in the southern hemisphere during August to September and has significant impacts on air quality and climate This columnar satellite-derived AOD can be related to ground-level PM2.5 using the following equation: AOD = PM2.5 ⋅ H ⋅ f ( R) ⋅ 3Qext ,dry ρ ⋅ reff , (20.1) where f (R) is the ratio of ambient and dry extinction coefficients, ρ is the aerosol mass density (g∙m–3), Qext,dry is the Mie extinction efficiency, reff is the particle effective radius that is an area weighted mean radius of the particle, and H is the boundary layer height This equation indicates that if the boundary layer height and other information about the atmosphere and aerosols are known, the satellite-retrieved AOD can be converted to ground-level PM2.5 During clear sky conditions and wellmixed boundary layer situations (typical during Terra and Aqua overpasses), AOD can be related to surface PM2.5 mass This relationship was explored by Wang and Christopher (2003) who correlated ground-level PM2.5 with AOD and found an excellent correlation between the two measures This relationship was then used to calculate air quality indices for the southeastern United States However, satellite remote sensing of PM air quality is a relatively new area of research As shown in Table 20.1, many previous studies have 483 Satellite Remote Sensing of Global Air Quality TABLE 20.1 Selected Relevant Literature Survey on Satellite Remote Sensing of Particulate Matter Air Quality # Reference Data and Study Area Wang and Christopher 2003 MODIS, seven stations, Alabama Chu et al 2003 AERONET, MODIS, PM10, station, Italy Engel-Cox et al 2004 MODIS, PM2.5 continental United States Hutchinson et al 2004 MODIS AOD maps, ozone, eastern United States Liu et al 2004 MISR, GEOS-CHEM GOCART, United States Liu et al 2005 MISR, GEOS-3 Meteorology, United States Al-Saadi et al 2005 MODIS, United States Hutchinson et al 2005 MODIS, Texas 10 Engel-Cox et al 2005 MODIS, United States Key Conclusions/Remarks Quantitative analysis with space and time collocated hourly PM2.5 and MODIS AOD Demonstrated the potential of satellite data for PM2.5 air quality monitoring (R = 0.7) Showed relationship between PM10 and AOD More qualitative discussion on satellite capabilities to detect and monitor aerosols globally (R = 0.82) Correlation analysis over entire United States and discuss difference in relationship over different regions Qualitative and quantitative analysis over larger area, demonstrated spatial distribution of correlation Range of R Used few MODIS AOD maps and discussed the hazy conditions, no correlation analysis, and more emphasis on ozone pollution First used MISR data for air quality study and have emphasis on seasonal and annual mean correlation analysis and forecasting (R = 0.78) Regression model development and forecasting of PM2.5, model generated coarse resolution meteorological fields are used and focused only in eastern United States 48% explanation of PM2.5 variations More descriptive paper on IDEA program, which provides online air quality conditions from MODIS and surface measurements over several locations in the United States Correlation analysis in Texas Correlation varies from 0.4 to 0.5 and long time averaging can make correlation greater than 0.9 Potential of satellite data for monitoring transport of PM2.5 over state boundaries and event-specific analysis (continued) 484 Environmental Remote Sensing and Systems Analysis TABLE 20.1 (Continued) Selected Relevant Literature Survey on Satellite Remote Sensing of Particulate Matter Air Quality # Reference Data and Study Area Key Conclusions/Remarks Correlation varies from 0.37 to 0.85 over different parts of the world Cloud fraction, relative humidity, and mixing height information can improve relationship significantly First study covered several global locations Weak correlation can be significantly improved by using vertical aerosol information from LIDAR measurements Intercomparison between MODIS and MISR over several locations in Canada and United States R = 0.69 (MODIS) and R = 0.58 (MISR) Different approach used to calculate the fine mass concentration Mainly focused on Europe Correlation varies from 0.5 for PM10 to 0.6 for PM2.5 Use of boundary layer height in analysis improved the relationship Intercomparison between MODIS and MISR in St Louis area MISR performed slightly better than MODIS in the region An attempt is made to improve AOD-PM2.5 relationship by refining MODIS AOD product, optimizing averaging area for MODIS pixels around surface station Multiregression analysis using modelderived meteorology shows improvement to PM2.5–AOD relationships 11 Gupta et al 2006 MODIS, meteorology, global 21 locations 12 Engel-Cox et al 2006 MODIS, lidar, United States 13 Van Donkelaar et al 2006 MODIS, MISR, PM2.5, GEOSCHEM, United States and global 14 Koelemeijer et al 2006 MODIS, PM2.5 and PM10, Europe 15 Liu et al 2007 MODIS, MISR, RUC 16 Hutchinson et al 2008 MODIS, lidar 17 Gupta and Christopher 2009a 18 Gupta and Christopher 2009b MODIS, RUC meteorology, southeast United States MODIS, southeast United States 19 Hoff and Christopher 2009 Review paper 20 Van Donkelaar et al 2010 Global Note: R = correlation coefficient A novel neural network method for assessing PM2.5 using satellite, groundbased and meteorological information A comprehensive review of particulate matter air pollution from spaceborne measurements MODIS, MISR and GEOS-CHEM aerosol vertical profiles Satellite Remote Sensing of Global Air Quality 485 shown the potential of using satellite-derived AOD information as surrogate for air quality conditions The two main conclusions from Table 20.1 indicate that (1) most of the studies have used MODIS-derived AOD products except for a few studies by Liu et al (2004, 2005) and Donkelaar et al (2006), which used AODs from both MISR and MODIS, and (2) the area for most of the studies has been in some part of the United States except studies by Gupta et al (2006), Koelemeijer et al (2006), and Donkelaar et al (2006) One of the reasons is that MODIS gives much better spatial and temporal coverage as compared to MISR, and measurements of PM2.5 mass concentration in other parts of the world are limited The first study by Wang and Christopher (2003) used PM2.5 mass and MODIS AOD data over seven stations in Alabama and presented very good correlation (>0.7) between these two parameters This study also concluded that, although deriving exact PM2.5 mass from satellite could have uncertainties, satellites can provide daily air quality indices with sufficient accuracies Chu et al (2003) were more focused on the qualitative analysis of the MODIS product as an alternative for air pollution in the regions where surface measurements are not available It also shows the potential of satellite monitoring of transport of air pollution from source to near and far urban areas Hutchison et al (2004, 2005) mainly focused on air quality over Texas and the eastern United States and on the use of satellite imagery in detecting and tracing the pollution A study by Engel-Cox et al (2004) presented a correlation analysis between MODIS AOD and PM2.5 mass over the entire United States The correlation pattern shows high values in the eastern and midwest portions of the United States, whereas correlations are low in western United States This study also concludes that high space- and time-resolved observations from satellites can provide synoptic information, visualization of the pollution, and validation of ground-based air quality data and estimations from models Engel-Cox et al also published other studies that further emphasize the use of satellite-derived aerosol products in day-today air quality monitoring and even in policy-related decision making One of these papers (Engel-Cox et al 2006) also presented the application of light detection and ranging (lidar-) derived vertical aerosol profiles to improve PM2.5–AOD relationship MODIS aerosol and cloud data are now being used in the Infusing Satellite Data into Environmental Applications (IDEA, http://www.star.nesdis.noaa.gov/smcd/spb/ aq/) program to monitor air quality over the United States IDEA is a joint effort by various federal agencies including NASA, National Oceanic and Atmospheric Administration (NOAA), and the EPA to improve air quality assessment, management, and prediction by infusing satellite measurements into analysis for public benefit (Al-Saadi et al 2005) MISR-derived aerosol products were first used by Liu et al (2004), which shows similar potential for air quality applications This study also used chemistry transport models to derive meteorological fields to examine their relative effects on PM2.5–AOD relationships Gupta et al (2006) compared the PM2.5–AOD relationship in different parts of the world such as Europe, Australia, the United States, and Asia This study shows applications of satellite-derived air quality products at global scales and in the regions where surface PM2.5 measurements are not available Correlations analysis varies in different parts of the world depending on accuracies 486 Environmental Remote Sensing and Systems Analysis –112 –104 –96 –104 –96 –88 –80 1H Terra –112 65.5 μg∙m–3) Several hundred miles away from the fire sources, in Birmingham, AL, the impact of the fires was also seen through the high AODs and PM2.5 values Correspondingly, PM2.5 mass due to organic carbon obtained from ground-based monitors showed a threefold increase during fire events Satellite data were especially critical in assessing PM2.5 air quality in areas where there were no ground-based monitors Satellite remote sensing was especially useful for various agencies including the U.S EPA during this event Another excellent example of how long-range transport of aerosols from one country affects another country was discussed by Wang et al (2006) Nearly 1.3 Tg of smoke from biomass burning was transported in Spring 2003 from the Yucatan Peninsula to downwind sources including the southeastern United States Satellite data sets were used to capture the spatial distribution of these aerosols and assess the magnitude of PM2.5 mass concentrations Satellite-derived fire emissions were also in numerical models to forecast PM2.5 mass As Hoff and Christopher (2009) noted in their review paper, satellite remote sensing has excellent potential for assessing surface PM2.5 20.5  CONCLUSIONS Satellite remote sensing is the only viable method for monitoring global air pollution While ground monitors are useful, spaceborne sensors can readily map columnar aerosol concentrations However, these columnar values can only be used when there are no clouds and bright surfaces such as snow/ice These columnar values can be related to ground-based PM mass if the vertical distributions of aerosols or boundary layer heights are known Previous studies have shown the promise and potential for monitoring global pollution from space, and future sensors will continue to improve Satellite Remote Sensing of Global Air Quality 489 our capabilities 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