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Abstract

Data from satellite instruments provide estimates of gas and particle levels relevant to human health, even pollutants invisible to the human eye. However, the successful interpretation of satellite data requires an understanding of how satellites relate to other data sources, as well as factors affecting their application to health challenges. Drawing from the expertise and experience of the 2016–2020 NASA HAQAST (Health and Air Quality Applied Sciences Team), we present a review of satellite data for air quality and health applications. We include a discussion of satellite data for epidemiological studies and health impact assessments, as well as the use of satellite data to evaluate air quality trends, support air quality regulation, characterize smoke from wildfires, and quantify emission sources. The primary advantage of satellite data compared to in situ measurements, e.g., from air quality monitoring stations, is their spatial coverage. Satellite data can reveal where pollution levels are highest around the world, how levels have changed over daily to decadal periods, and where pollutants are transported from urban to global scales. To date, air quality and health applications have primarily utilized satellite observations and satellite-derived products relevant to near-surface particulate matter <2.5 μm in diameter (PM) and nitrogen dioxide (NO). Health and air quality communities have grown increasingly engaged in the use of satellite data, and this trend is expected to continue. From health researchers to air quality managers, and from global applications to community impacts, satellite data are transforming the way air pollution exposure is evaluated.

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2021-07-20
2024-04-15
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Literature Cited

  1. 1. 
    Holloway T, Jacob DJ, Miller D. 2018. Short history of NASA applied science teams for air quality and health. J. Appl. Remote Sens. 12:4042611
    [Google Scholar]
  2. 2. 
    Cromar KR, Duncan BN, Bartonova A, Benedict K, Brauer M et al. 2019. Air pollution monitoring for health research and patient care. An official American Thoracic Society workshop report. Ann. Am. Thorac. Soc. 16:101207–14
    [Google Scholar]
  3. 3. 
    Miranda ML, Edwards SE, Keating MH, Paul CJ. 2011. Making the environmental justice grade: the relative burden of air pollution exposure in the United States. Int. J. Environ. Res. Public Health 8:61755–71
    [Google Scholar]
  4. 4. 
    Martin RV, Brauer M, van Donkelaar A, Shaddick G, Narain U, Dey S. 2019. No one knows which city has the highest concentration of fine particulate matter. Atmos. Environ. X 3:100040
    [Google Scholar]
  5. 5. 
    Shaddick G, Thomas ML, Green A, Brauer M, Donkelaar A et al. 2018. Data integration model for air quality: a hierarchical approach to the global estimation of exposures to ambient air pollution. J. R. Stat. Soc. C 67:1231–53
    [Google Scholar]
  6. 6. 
    OECD (Organ. Econ. Co-op. Develop.) 2016. The Economic Consequences of Outdoor Air Pollution. Paris: OECD
  7. 7. 
    de Sherbinin A, Levy MA, Zell E, Weber S, Jaiteh M. 2014. Using satellite data to develop environmental indicators. Environ. Res. Lett. 9:084013
    [Google Scholar]
  8. 8. 
    Duncan BN, Prados AI, Lamsal LN, Liu Y, Streets DG et al. 2014. Satellite data of atmospheric pollution for U.S. air quality applications: examples of applications, summary of data end-user resources, answers to FAQs, and common mistakes to avoid. Atmos. Environ. 94:647–62
    [Google Scholar]
  9. 9. 
    Shaddick G, Thomas ML, Amini H, Broday D, Cohen A et al. 2018. Data integration for the assessment of population exposure to ambient air pollution for global burden of disease assessment. Environ. Sci. Technol. 52:169069–78
    [Google Scholar]
  10. 10. 
    Diao M, Holloway T, Choi S, O'Neill SM, Al-Hamdan MZ et al. 2019. Methods, availability, and applications of PM2.5 exposure estimates derived from ground measurements, satellite, and atmospheric models. J. Air Waste Manag. Assoc. 69:121391–414
    [Google Scholar]
  11. 11. 
    Wang J, Christopher SA. 2003. Intercomparison between satellite-derived aerosol optical thickness and PM2.5 mass: implications for air quality studies. Geophys. Res. Lett. 30:212095
    [Google Scholar]
  12. 12. 
    Gupta P, Christopher SA. 2009. Particulate matter air quality assessment using integrated surface, satellite, and meteorological products: 2. A neural network approach. J. Geophys. Res. Atmos. 114:D20D20205
    [Google Scholar]
  13. 13. 
    Al-Hamdan MZ, Crosson WL, Limaye AS, Rickman DL, Quattrochi DA et al. 2009. Methods for characterizing fine particulate matter using ground observations and remotely sensed data: potential use for environmental public health surveillance. J. Air Waste Manag. Assoc. 59:7865–81
    [Google Scholar]
  14. 14. 
    Liu Y, Franklin M, Kahn R, Koutrakis P. 2007. Using aerosol optical thickness to predict ground-level PM2.5 concentrations in the St. Louis area: a comparison between MISR and MODIS. Remote Sens. Environ. 107:133–44
    [Google Scholar]
  15. 15. 
    Paciorek CJ, Liu Y, Moreno-Macias H, Kondragunta S. 2008. Spatiotemporal associations between GOES aerosol optical depth retrievals and ground-level PM2.5. Environ. Sci. Technol. 42:155800–6
    [Google Scholar]
  16. 16. 
    Hu X, Waller LA, Lyapustin A, Wang Y, Al-Hamdan MZ et al. 2014. Estimating ground-level PM2.5 concentrations in the Southeastern United States using MAIAC AOD retrievals and a two-stage model. Remote Sens. Environ. 140:220–32
    [Google Scholar]
  17. 17. 
    Ma Z, Hu X, Huang L, Bi J, Liu Y. 2014. Estimating ground-level PM2.5 in China using satellite remote sensing. Environ. Sci. Technol. 48:137436–44
    [Google Scholar]
  18. 18. 
    Zhang G, Rui X, Fan Y. 2018. Critical review of methods to estimate PM2.5 concentrations within specified research region. ISPRS Int. J. Geo-Inf. 7:9368
    [Google Scholar]
  19. 19. 
    Meng X, Hand JL, Schichtel BA, Liu Y. 2018. Space-time trends of PM2.5 constituents in the conterminous United States estimated by a machine learning approach, 2005–2015. Environ. Int. 121:21137–47
    [Google Scholar]
  20. 20. 
    Bi J, Stowell J, Seto EYW, English PB, Al-Hamdan MZ et al. 2020. Contribution of low-cost sensor measurements to the prediction of PM2.5 levels: a case study in Imperial County, California, USA. Environ. Res. 180:108810
    [Google Scholar]
  21. 21. 
    Hu X, Belle JH, Meng X, Wildani A, Waller LA et al. 2017. Estimating PM2.5 concentrations in the conterminous United States using the random forest approach. Environ. Sci. Technol. 51:126936–44
    [Google Scholar]
  22. 22. 
    Liang F, Xiao Q, Huang K, Yang X, Liu F et al. 2020. The 17-y spatiotemporal trend of PM2.5 and its mortality burden in China. PNAS 117:4125601–8
    [Google Scholar]
  23. 23. 
    Meng X, Liu C, Zhang L, Wang W, Stowell J et al. 2021. Estimating PM2.5 concentrations in Northeastern China with full spatiotemporal coverage, 2005–2016. Remote Sens. Environ. 253:112203
    [Google Scholar]
  24. 24. 
    Stafoggia M, Bellander T, Bucci S, Davoli M, de Hoogh K et al. 2019. Estimation of daily PM10 and PM2.5 concentrations in Italy, 2013–2015, using a spatiotemporal land-use random-forest model. Environ. Int. 124:170–79
    [Google Scholar]
  25. 25. 
    Wang L, Bi J, Meng X, Geng G, Huang K et al. 2020. Satellite-based assessment of the long-term efficacy of PM2.5 pollution control policies across the Taiwan Strait. Remote Sens. Environ. 251:112067
    [Google Scholar]
  26. 26. 
    Just A, De Carli M, Shtein A, Dorman M, Lyapustin A, Kloog I 2018. Correcting measurement error in satellite aerosol optical depth with machine learning for modeling PM2.5 in the Northeastern USA. Remote Sens. 10:5803
    [Google Scholar]
  27. 27. 
    Li T, Shen H, Yuan Q, Zhang X, Zhang L. 2017. Estimating ground-level PM2.5 by fusing satellite and station observations: a geo-intelligent deep learning approach: deep learning for PM2.5 estimation. Geophys. Res. Lett. 44:2311985–93
    [Google Scholar]
  28. 28. 
    Park Y, Kwon B, Heo J, Hu X, Liu Y, Moon T. 2020. Estimating PM2.5 concentration of the conterminous United States via interpretable convolutional neural networks. Environ. Pollut. 256:113395
    [Google Scholar]
  29. 29. 
    Li L, Girguis M, Lurmann F, Pavlovic N, McClure C et al. 2020. Ensemble-based deep learning for estimating PM2.5 over California with multisource big data including wildfire smoke. Environ. Int. 145:106143
    [Google Scholar]
  30. 30. 
    Xiao Q, Chang HH, Geng G, Liu Y. 2018. An ensemble machine-learning model to predict historical PM2.5 concentrations in China from satellite data. Environ. Sci. Technol. 52:2213260–69
    [Google Scholar]
  31. 31. 
    Jin X, Fiore AM, Civerolo K, Bi J, Liu Y et al. 2019. Comparison of multiple PM2.5 exposure products for estimating health benefits of emission controls over New York State, USA. Environ. Res. Lett. 14:8084023
    [Google Scholar]
  32. 32. 
    Kelly JT, Jang C, Timin B, Di Q, Schwartz J et al. 2020. Examining PM2.5 concentrations and exposure using multiple models. Environ. Res. In press. https://doi.org/10.1016/j.envres.2020.110432
    [Crossref] [Google Scholar]
  33. 33. 
    Berrocal VJ, Guan Y, Muyskens A, Wang H, Reich BJ et al. 2020. A comparison of statistical and machine learning methods for creating national daily maps of ambient PM2.5 concentration. Atmos. Environ. 222:117130
    [Google Scholar]
  34. 34. 
    Snider G, Weagle CL, Murdymootoo KK, Ring A, Ritchie Y et al. 2016. Variation in global chemical composition of PM2.5: emerging results from SPARTAN. Atmospheric Chem. Phys. 16:159629–53
    [Google Scholar]
  35. 35. 
    Crumeyrolle S, Chen G, Ziemba L, Beyersdorf A, Thornhill L et al. 2014. Factors that influence surface PM2.5 values inferred from satellite observations: perspective gained for the US Baltimore–Washington metropolitan area during DISCOVER-AQ. Atmos. Chem. Phys. 14:42139–53
    [Google Scholar]
  36. 36. 
    Streets DG, Canty T, Carmichael GR, de Foy B, Dickerson RR et al. 2013. Emissions estimation from satellite retrievals: a review of current capability. Atmos. Environ. 77:1011–42
    [Google Scholar]
  37. 37. 
    Strickland MJ, Hao H, Hu X, Chang HH, Darrow LA, Liu Y. 2016. Pediatric emergency visits and short-term changes in PM2.5 concentrations in the U.S. state of Georgia. Environ. Health Perspect. 124:5690–96
    [Google Scholar]
  38. 38. 
    Stowell JD, Geng G, Saikawa E, Chang HH, Fu J et al. 2019. Associations of wildfire smoke PM2.5 exposure with cardiorespiratory events in Colorado 2011–2014. Environ. Int. 133:Pt. A105151
    [Google Scholar]
  39. 39. 
    Geng G, Murray NL, Tong D, Fu JS, Hu X et al. 2018. Satellite-based daily PM2.5 estimates during fire seasons in Colorado. J. Geophys. Res. Atmos. 123:158159–71
    [Google Scholar]
  40. 40. 
    Xiao Q, Chen H, Strickland MJ, Kan H, Chang HH et al. 2018. Associations between birth outcomes and maternal PM2.5 exposure in Shanghai: a comparison of three exposure assessment approaches. Environ. Int. 117:226–36
    [Google Scholar]
  41. 41. 
    Di Q, Dai L, Wang Y, Zanobetti A, Choirat C et al. 2017. Association of short-term exposure to air pollution with mortality in older adults. JAMA 318:242446
    [Google Scholar]
  42. 42. 
    Huang K, Liang F, Yang X, Liu F, Li J et al. 2019. Long term exposure to ambient fine particulate matter and incidence of stroke: prospective cohort study from the China-PAR project. BMJ 367:l6720
    [Google Scholar]
  43. 43. 
    Liang F, Liu F, Huang K, Yang X, Li J et al. 2020. Long-term exposure to fine particulate matter and cardiovascular disease in China. J. Am. Coll. Cardiol. 75:7707–17
    [Google Scholar]
  44. 44. 
    Tapia VL, Vasquez BV, Vu B, Liu Y, Steenland K, Gonzales GF. 2020. Association between maternal exposure to particulate matter (PM2.5) and adverse pregnancy outcomes in Lima, Peru. J. Expo. Sci. Environ. Epidemiol. 30:4689–97
    [Google Scholar]
  45. 45. 
    Tapia V, Steenland K, Sarnat SE, Vu B, Liu Y et al. 2020. Time-series analysis of ambient PM2.5 and cardiorespiratory emergency room visits in Lima, Peru during 2010–2016. J. Expo. Sci. Environ. Epidemiol. 30:4680–88
    [Google Scholar]
  46. 46. 
    Tapia V, Steenland K, Vu B, Liu Y, Vásquez V, Gonzales GF. 2020. PM2.5 exposure on daily cardio-respiratory mortality in Lima, Peru, from 2010 to 2016. Environ. Health Glob. Access Sci. Source 19:163
    [Google Scholar]
  47. 47. 
    Heft-Neal S, Burney J, Bendavid E, Burke M. 2018. Robust relationship between air quality and infant mortality in Africa. Nature 559:7713254–58
    [Google Scholar]
  48. 48. 
    Khreis H, Kelly C, Tate J, Parslow R, Lucas K, Nieuwenhuijsen M. 2017. Exposure to traffic-related air pollution and risk of development of childhood asthma: a systematic review and meta-analysis. Environ. Int. 100:1–31
    [Google Scholar]
  49. 49. 
    Lamsal LN, Duncan BN, Yoshida Y, Krotkov NA, Pickering KE et al. 2015. U.S. NO2 trends (2005–2013): EPA Air Quality System (AQS) data versus improved observations from the Ozone Monitoring Instrument (OMI). Atmos. Environ. 110:130–43
    [Google Scholar]
  50. 50. 
    Cooper MJ, Martin RV, McLinden CA, Brook JR. 2020. Inferring ground-level nitrogen dioxide concentrations at fine spatial resolution applied to the TROPOMI satellite instrument. Environ. Res. Lett. 15:10104013
    [Google Scholar]
  51. 51. 
    Richmond-Bryant J, Owen RC, Graham S, Snyder M, McDow S et al. 2017. Estimation of on-road NO2 concentrations, NO2/NOX ratios, and related roadway gradients from near-road monitoring data. Air Qual. Atmos. Health 10:5611–25
    [Google Scholar]
  52. 52. 
    Anderson HR, Favarato G, Atkinson RW. 2013. Erratum to: Long-term exposure to air pollution and the incidence of asthma: meta-analysis of cohort studies. Air Qual. Atmos. Health 6:2541–42
    [Google Scholar]
  53. 53. 
    HEI (Health Effects Inst.) 2010. Traffic-related air pollution: a critical review of the literature on emissions, exposure, and health effects Spec. Rep. 17 HEI: Boston
  54. 54. 
    Larkin A, Geddes JA, Martin RV, Xiao Q, Liu Y et al. 2017. Global land use regression model for nitrogen dioxide air pollution. Environ. Sci. Technol. 51:126957–64
    [Google Scholar]
  55. 55. 
    Franklin M, Kalashnikova OV, Garay MJ. 2017. Size-resolved particulate matter concentrations derived from 4.4 km-resolution size-fractionated Multi-angle Imaging SpectroRadiometer (MISR) aerosol optical depth over Southern California. Remote Sens. Environ. 196:312–23
    [Google Scholar]
  56. 56. 
    Geng G, Meng X, He K, Liu Y. 2020. Random forest models for PM2.5 speciation concentrations using MISR fractional AODs. Environ. Res. Lett. 15:3034056
    [Google Scholar]
  57. 57. 
    Liu Y, Koutrakis P, Kahn R. 2007. Estimating fine particulate matter component concentrations and size distributions using satellite-retrieved fractional aerosol optical depth: part 1—method development. J. Air Waste Manag. Assoc. 57:111351–59
    [Google Scholar]
  58. 58. 
    Liu Y, Koutrakis P, Kahn R, Turquety S, Yantosca RM. 2007. Estimating fine particulate matter component concentrations and size distributions using satellite-retrieved fractional aerosol optical depth: part 2—a case study. J. Air Waste Manag. Assoc. 57:111360–69
    [Google Scholar]
  59. 59. 
    Diner DJ, Boland SW, Brauer M, Bruegge C, Burke KA et al. 2018. Advances in multiangle satellite remote sensing of speciated airborne particulate matter and association with adverse health effects: from MISR to MAIA. J. Appl. Remote Sens. 12:4042603
    [Google Scholar]
  60. 60. 
    Liu Y, Diner DJ. 2017. Multi-angle imager for aerosols: a satellite investigation to benefit public health. Public Health Rep 132:114–17
    [Google Scholar]
  61. 61. 
    van Donkelaar A, Martin RV, Brauer M, Kahn R, Levy R et al. 2010. Global estimates of ambient fine particulate matter concentrations from satellite-based aerosol optical depth: development and application. Environ. Health Perspect. 118:6847–55
    [Google Scholar]
  62. 62. 
    Evans J, van Donkelaar A, Martin RV, Burnett R, Rainham DG et al. 2013. Estimates of global mortality attributable to particulate air pollution using satellite imagery. Environ. Res. 120:33–42
    [Google Scholar]
  63. 63. 
    Cohen AJ, Brauer M, Burnett R, Anderson HR, Frostad J et al. 2017. Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: an analysis of data from the Global Burden of Diseases Study 2015. Lancet 389:100821907–18
    [Google Scholar]
  64. 64. 
    Malley CS, Kuylenstierna JCI, Vallack HW, Henze DK, Blencowe H, Ashmore MR. 2017. Preterm birth associated with maternal fine particulate matter exposure: a global, regional and national assessment. Environ. Int. 101:173–82
    [Google Scholar]
  65. 65. 
    Anenberg SC, Miller J, Minjares R, Du L, Henze DK et al. 2017. Impacts and mitigation of excess diesel-related NOx emissions in 11 major vehicle markets. Nature 545:7655467–71
    [Google Scholar]
  66. 66. 
    Anenberg SC, Miller J, Henze DK, Minjares R, Achakulwisut P. 2019. The global burden of transportation tailpipe emissions on air pollution-related mortality in 2010 and 2015. Environ. Res. Lett. 14:9094012
    [Google Scholar]
  67. 67. 
    Lacey FG, Henze DK, Lee CJ, van Donkelaar A, Martin RV. 2017. Transient climate and ambient health impacts due to national solid fuel cookstove emissions. PNAS 114:61269–74
    [Google Scholar]
  68. 68. 
    Brauer M, Freedman G, Frostad J, van Donkelaar A, Martin RV et al. 2016. Ambient air pollution exposure estimation for the global burden of disease 2013. Environ. Sci. Technol. 50:179–88
    [Google Scholar]
  69. 69. 
    van Donkelaar A, Martin RV, Brauer M, Hsu NC, Kahn RA et al. 2016. Global estimates of fine particulate matter using a combined geophysical-statistical method with information from satellites, models, and monitors. Environ. Sci. Technol. 50:73762–72
    [Google Scholar]
  70. 70. 
    Stanaway JD, Afshin A, Gakidou E, Lim SS, Abate D et al. 2018. Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet 392:101591923–94
    [Google Scholar]
  71. 71. 
    Burnett RT, Pope CA, Ezzati M, Olives C, Lim SS et al. 2014. An integrated risk function for estimating the global burden of disease attributable to ambient fine particulate matter exposure. Environ. Health Perspect. 122:4397–403
    [Google Scholar]
  72. 72. 
    Yin P, Brauer M, Cohen A, Burnett RT, Liu J et al. 2017. Long-term fine particulate matter exposure and nonaccidental and cause-specific mortality in a large national cohort of Chinese men. Environ. Health Perspect. 125:11117002
    [Google Scholar]
  73. 73. 
    Li T, Zhang Y, Wang J, Xu D, Yin Z et al. 2018. All-cause mortality risk associated with long-term exposure to ambient PM2.5 in China: a cohort study. Lancet Public Health 3:10e470–77
    [Google Scholar]
  74. 74. 
    U.N. Environ. Assem 2014. Resolutions and decisions adopted by the United Nations Environment Assembly of the United Nations Environment Programme at its first session on 27 June 2014. Tech. Rep., U.N. Geneva: https://www.unep.org/resources/report/resolutions-and-decisions-adopted-united-nations-environment-assembly-united
    [Google Scholar]
  75. 75. 
    World Health Assem. 68 2015. Health and the environment: addressing the health impact of air pollution: draft resolution proposed by the delegations of Albania, Chile, Colombia, France, Germany, Monaco, Norway, Panama, Sweden, Switzerland, Ukraine, United States of America, Uruguay and Zambia Tech. Rep. A68/A/CONF./2 Rev.1 World Health Organ. Geneva:
  76. 76. 
    Li M, Klimont Z, Zhang Q, Martin RV, Zheng B et al. 2018. Comparison and evaluation of anthropogenic emissions of SO2 and NOX over China. Atmos. Chem. Phys. 18:53433–56
    [Google Scholar]
  77. 77. 
    Zheng B, Tong D, Li M, Liu F, Hong C et al. 2018. Trends in China's anthropogenic emissions since 2010 as the consequence of clean air actions. Atmos. Chem. Phys. 18:1914095–111
    [Google Scholar]
  78. 78. 
    Ciarelli G, Colette A, Schucht S, Beekmann M, Andersson C et al. 2019. Long-term health impact assessment of total PM2.5 in Europe during the 1990–2015 period. Atmos. Environ. X 3:100032
    [Google Scholar]
  79. 79. 
    Liousse C, Assamoi E, Criqui P, Granier C, Rosset R. 2014. Explosive growth in African combustion emissions from 2005 to 2030. Environ. Res. Lett. 9:3035003
    [Google Scholar]
  80. 80. 
    Achakulwisut P, Brauer M, Hystad P, Anenberg SC. 2019. Global, national, and urban burdens of paediatric asthma incidence attributable to ambient NO2 pollution: estimates from global datasets. Lancet Planet. Health 3:4e166–78
    [Google Scholar]
  81. 81. 
    Zhang Y, Cooper OR, Gaudel A, Thompson AM, Nédélec P et al. 2016. Tropospheric ozone change from 1980 to 2010 dominated by equatorward redistribution of emissions. Nat. Geosci. 9:12875–79
    [Google Scholar]
  82. 82. 
    Duncan BN, Lamsal LN, Thompson AM, Yoshida Y, Lu Z et al. 2016. A space-based, high-resolution view of notable changes in urban NOx pollution around the world (2005–2014). J. Geophys. Res. Atmos. 121:2976–96
    [Google Scholar]
  83. 83. 
    Li C, McLinden C, Fioletov V, Krotkov N, Carn S et al. 2017. India is overtaking China as the world's largest emitter of anthropogenic sulfur dioxide. Sci. Rep. 7:14304
    [Google Scholar]
  84. 84. 
    Montgomery A, Holloway T. 2018. Assessing the relationship between satellite-derived NO2 and economic growth over the 100 most populous global cities. J. Appl. Remote Sens. 12:4042607
    [Google Scholar]
  85. 85. 
    Bauwens M, Compernolle S, Stavrakou T, Muller JF, van Gent J et al. 2020. Impact of coronavirus outbreak on NO2 pollution assessed using TROPOMI and OMI observations. Geophys. Res. Lett. 47:11e2020GL087978
    [Google Scholar]
  86. 86. 
    Verstraeten WW, Neu JL, Williams JE, Bowman KW, Worden JR, Boersma KF. 2015. Rapid increases in tropospheric ozone production and export from China. Nat. Geosci. 8:9690–95
    [Google Scholar]
  87. 87. 
    Puchalski MA, Walker JT, Beachley GM, Zondlo MA, Benedict KB et al. 2019. Need for improved monitoring of spatial and temporal trends of reduced nitrogen. EM Mag. Environ. Manag. 2019:July7–10
    [Google Scholar]
  88. 88. 
    Wang R, Guo X, Pan D, Kelly JT, Bash JO et al. 2020. Monthly patterns of ammonia over the contiguous United States at 2-km resolution. Geophys. Res. Lett. 48:5e2020GL090579
    [Google Scholar]
  89. 89. 
    Zhu L, Jacob DJ, Kim PS, Fisher JA, Yu K et al. 2016. Observing atmospheric formaldehyde (HCHO) from space: validation and intercomparison of six retrievals from four satellites (OMI, GOME2A, GOME2B, OMPS) with SEAC4RS aircraft observations over the southeast US. Atmos. Chem. Phys. 16:2113477–90
    [Google Scholar]
  90. 90. 
    Fu D, Millet DB, Wells KC, Payne VH, Yu S et al. 2019. Direct retrieval of isoprene from satellite-based infrared measurements. Nat. Commun. 10:13811
    [Google Scholar]
  91. 91. 
    EPA (Environ. Protect. Agency) 2020. Our nation's air Tech. Rep., EPA Washington, DC:
  92. 92. 
    TCEQ (Texas Comm. Environ. Qual.) 2016. Houston-Galveston-Brazoria attainment demonstration State Implementation Plan revision for the 2008 eight-hour ozone standard nonattainment area Proj. Number 2016-016-SIP-NR, TCEQ Austin, TX:
  93. 93. 
    Conn. Dep. Energy Environ. Protect 2017. Enclosure A: revision to Connecticut's state implementation plan: 8-hour ozone attainment demonstration for the Connecticut portion of the New York-northern New Jersey-Long Island (NY-NJ-CT) nonattainment area Tech. Support Doc. Connect. Dep. Energy Environ. Protect. Hartford, CT:
  94. 94. 
    Conn. Dep. Energy Environ. Protect 2017. Enclosure A : revision to Connecticut's state implementation plan: 8-hour ozone attainment demonstration for the greater Connecticut nonattainment area. Tech. Support Doc. Connect. Dep. Energy Environ. Protect. Hartford, CT:
    [Google Scholar]
  95. 95. 
    Geigert M. 2018. Guide to using satellite images in support of exceptional event demonstrations Tech. Guid. Doc. 2 NASA Health Air Qual. Appl. Sci. Team Washington, DC:
  96. 96. 
    Fiore AM, Pierce R, Dickerson R, Lin M. 2014. Detecting and attributing episodic high background ozone events. EM Mag. Environ. Manag. 2014:Feb.22–28
    [Google Scholar]
  97. 97. 
    Kahn R. 2020. A global perspective on wildfires. Eos Jan. 27. https://eos.org/science-updates/a-global-perspective-on-wildfires
    [Google Scholar]
  98. 98. 
    Ryan KC, Knapp EE, Varner JM. 2013. Prescribed fire in North American forests and woodlands: history, current practice, and challenges. Front. Ecol. Environ. 11:S1e15–24
    [Google Scholar]
  99. 99. 
    Reid CE, Brauer M, Johnston FH, Jerrett M, Balmes JR, Elliott CT. 2016. Critical review of health impacts of wildfire smoke exposure. Environ. Health Perspect. 124:91334–43
    [Google Scholar]
  100. 100. 
    Larsen AE, Reich BJ, Ruminski M, Rappold AG. 2018. Impacts of fire smoke plumes on regional air quality, 2006–2013. J. Expo. Sci. Environ. Epidemiol. 28:4319–27
    [Google Scholar]
  101. 101. 
    Fann N, Alman B, Broome RA, Morgan GG, Johnston FH et al. 2018. The health impacts and economic value of wildland fire episodes in the U.S.: 2008–2012. Sci. Total Environ. 610–611:802–9
    [Google Scholar]
  102. 102. 
    Jaffe DA, O'Neill SM, Larkin NK, Holder AL, Peterson DL et al. 2020. Wildfire and prescribed burning impacts on air quality in the United States. J. Air Waste Manag. Assoc. 70:6583–615
    [Google Scholar]
  103. 103. 
    Hu Y, Odman MT, Chang ME, Jackson W, Lee S et al. 2008. Simulation of air quality impacts from prescribed fires on an urban area. Environ. Sci. Technol. 42:103676–82
    [Google Scholar]
  104. 104. 
    DA Jaffe, Wigder NL. 2012. Ozone production from wildfires: a critical review. Atmos. Environ. 51:1–10
    [Google Scholar]
  105. 105. 
    Conn. Dep. Energy Environ. Protect 2017. May 2016 ozone exceptional event analysis. Tech. Support Doc Conn. Dep. Energy Environ. Protect. Hartford, CT:
    [Google Scholar]
  106. 106. 
    Chen J, Vaughan J, Avise J, O'Neill S, Lamb B 2008. Enhancement and evaluation of the AIRPACT ozone and PM2.5 forecast system for the Pacific Northwest. J. Geophys. Res. Atmos. 113:D14D14305
    [Google Scholar]
  107. 107. 
    Larkin NK, O'Neill SM, Solomon R, Raffuse S, Strand T et al. 2009. The BlueSky smoke modeling framework. Int. J. Wildland Fire 18:8906–20
    [Google Scholar]
  108. 108. 
    Chen J, Anderson K, Pavlovic R, Moran MD, Englefield P et al. 2019. The FireWork v2.0 air quality forecast system with biomass burning emissions from the Canadian Forest Fire Emissions Prediction System v2. .03. Geosci. Model. Dev. 1273283–310
  109. 109. 
    Grell G, Freitas SR, Stuefer M, Fast J. 2011. Inclusion of biomass burning in WRF-Chem: impact of wildfires on weather forecasts. Atmos. Chem. Phys. 11:115289–303
    [Google Scholar]
  110. 110. 
    Lee P, McQueen J, Stajner I, Huang J, Pan L et al. 2017. NAQFC developmental forecast guidance for fine particulate matter (PM2.5). Weather Forecast 32:1343–60
    [Google Scholar]
  111. 111. 
    Odman OT, Huang R, Pophale AA, Sakhpara RD, Hu Y et al. 2018. Forecasting the impacts of prescribed fires for dynamic air quality management. Atmosphere 9:6220
    [Google Scholar]
  112. 112. 
    Ichoku C, Ellison L. 2014. Global top-down smoke-aerosol emissions estimation using satellite fire radiative power measurements. Atmos. Chem. Phys. 14:136643–67
    [Google Scholar]
  113. 113. 
    Sofiev M, Ermakova T, Vankevich R. 2012. Evaluation of the smoke-injection height from wild-land fires using remote-sensing data. Atmos. Chem. Phys. 12:41995–2006
    [Google Scholar]
  114. 114. 
    Li F, Zhang X, Roy DP, Kondragunta S. 2019. Estimation of biomass-burning emissions by fusing the fire radiative power retrievals from polar-orbiting and geostationary satellites across the conterminous United States. Atmos. Environ. 211:274–87
    [Google Scholar]
  115. 115. 
    Cleland SE, West JJ, Jia Y, Reid S, Raffuse S et al. 2020. Estimating wildfire smoke concentrations during the October 2017 California fires through BME space/time data fusion of observed, modeled, and satellite-derived PM2.5. Environ. Sci. Technol. 54:2113439–47
    [Google Scholar]
  116. 116. 
    Hunt W, Winker D, Vaughan M, Powell K, Lucker P, Weimer C. 2009. CALIPSO lidar description and performance assessment. J. Atmos. Ocean. Technol. 26:1214–28
    [Google Scholar]
  117. 117. 
    Diner D, Beckert J, Reilly TH, Bruegge C, Conel JE et al. 1998. Multi-angle Imaging SpectroRadiometer (MISR) instrument description and experiment overview. IEEE Trans. Geosci. Remote Sens. 36:1072–87
    [Google Scholar]
  118. 118. 
    Lyapustin A, Wang Y, Korkin S, Kahn R, Winker D. 2020. MAIAC thermal technique for smoke injection height from MODIS. IEEE Geosci. Remote Sens. Lett. 17:5730–34
    [Google Scholar]
  119. 119. 
    Marsha A, Larkin NK. 2019. A statistical model for predicting PM2.5 for the western United States. J. Air Waste Manag. Assoc. 69:101215–29
    [Google Scholar]
  120. 120. 
    Tian D, Wang Y, Bergin M, Hu Y, Liu Y, Russell AG. 2008. Air quality impacts from prescribed forest fires under different management practices. Environ. Sci. Technol. 42:82767–72
    [Google Scholar]
  121. 121. 
    Sarnat JA, Marmur A, Klein M, Kim E, Russell AG et al. 2008. Fine particle sources and cardiorespiratory morbidity: an application of chemical mass balance and factor analytical source-apportionment methods. Environ. Health Perspect. 116:4459–66
    [Google Scholar]
  122. 122. 
    Bates JT, Weber RJ, Abrams J, Verma V, Fang T et al. 2015. Reactive oxygen species generation linked to sources of atmospheric particulate matter and cardiorespiratory effects. Environ. Sci. Technol. 49:2213605–12
    [Google Scholar]
  123. 123. 
    Bates JT, Fang T, Verma V, Zeng L, Weber RJ et al. 2019. Review of acellular assays of ambient particulate matter oxidative potential: methods and relationships with composition, sources, and health effects. Environ. Sci. Technol. 53:84003–19
    [Google Scholar]
  124. 124. 
    Hu Y, Ai HH, Odman MT, Vaidyanathan A, Russell AG. 2019. Development of a WebGIS-based analysis tool for human health protection from the impacts of prescribed fire smoke in Southeastern USA. Int. J. Environ. Res. Public Health 16:111981
    [Google Scholar]
  125. 125. 
    Huang R, Hu Y, Russell AG, Mulholland JA, Odman MT. 2019. The impacts of prescribed fire on PM2.5 air quality and human health: application to asthma-related emergency room visits in Georgia, USA. Int. J. Environ. Res. Public Health 16:132312–12
    [Google Scholar]
  126. 126. 
    Johnson Gaither C, Afrin S, Garcia-Menendez F, Odman MT, Huang R et al. 2019. African American exposure to prescribed fire smoke in Georgia, USA. Int. J. Environ. Res. Public Health 16:173079
    [Google Scholar]
  127. 127. 
    Crooks JL, Cascio WE, Percy MS, Reyes J, Neas LM, Hilborn ED. 2016. The association between dust storms and daily non-accidental mortality in the United States, 1993–2005. Environ. Health Perspect. 124:111735–43
    [Google Scholar]
  128. 128. 
    Rice MB, Mittleman MA. 2017. Dust storms, heart attacks, and protecting those at risk. Eur. Heart J. 38:433209–10
    [Google Scholar]
  129. 129. 
    García-Pando CP, Stanton MC, Diggle PJ, Trzaska S, Miller RL et al. 2014. Soil dust aerosols and wind as predictors of seasonal meningitis incidence in Niger. Environ. Health Perspect. 122:7679–86
    [Google Scholar]
  130. 130. 
    Tong DQ, Wang JXL, Gill TE, Lei H, Wang B. 2017. Intensified dust storm activity and Valley fever infection in the southwestern United States. Geophys. Res. Lett. 44:94304–12
    [Google Scholar]
  131. 131. 
    Tong DQ, Dan M, Wang T, Lee P 2012. Long-term dust climatology in the western United States reconstructed from routine aerosol ground monitoring. Atmos. Chem. Phys. 12:115189–205
    [Google Scholar]
  132. 132. 
    Prospero JM, Ginoux P, Torres O, Nicholson SE, Gill TE. 2002. Environmental characterization of global sources of atmospheric soil dust identified with the Nimbus 7 Total Ozone Mapping Spectrometer (TOMS) absorbing aerosol product. Rev. Geophys. 40:12–31
    [Google Scholar]
  133. 133. 
    Rivera Rivera NI, Gill TE, Bleiweiss MP, Hand JL 2010. Source characteristics of hazardous Chihuahuan Desert dust outbreaks. Atmos. Environ. 44:202457–68
    [Google Scholar]
  134. 134. 
    Baddock MC, Gill TE, Bullard JE, Acosta MD, Rivera NIR. 2011. Geomorphology of the Chihuahuan Desert based on potential dust emissions. J. Maps 7:1249–59
    [Google Scholar]
  135. 135. 
    Lee JA, Gill TE, Mulligan KR, Dominguez Acosta M, Perez AE 2009. Land use/land cover and point sources of the 15 December 2003 dust storm in southwestern North America. Geomorphology 105:118–27
    [Google Scholar]
  136. 136. 
    Ginoux P, Prospero JM, Gill TE, Hsu NC, Zhao M. 2012. Global-scale attribution of anthropogenic and natural dust sources and their emission rates based on MODIS Deep Blue aerosol products. Rev. Geophys. 50:31–36
    [Google Scholar]
  137. 137. 
    Warm K, Hedman L, Lindberg A, Lötvall J, Lundbäck B, Rönmark E. 2015. Allergic sensitization is age-dependently associated with rhinitis, but less so with asthma. J. Allergy Clin. Immunol. 136:61559–65.e2
    [Google Scholar]
  138. 138. 
    Bartra J, Mullol J, del Cuvillo A, Dávila I, Ferrer M et al. 2007. Air pollution and allergens. J. Investig. Allergol. Clin. Immunol. 17:Suppl. 23–8
    [Google Scholar]
  139. 139. 
    Linneberg A, Dam Petersen K, Hahn-Pedersen J, Hammerby E, Serup-Hansen N, Boxall N 2016. Burden of allergic respiratory disease: a systematic review. Clin. Mol. Allergy 14:112
    [Google Scholar]
  140. 140. 
    Steinsvaag SK. 2012. Allergic rhinitis: an updated overview. Curr. Allergy Asthma Rep. 12:299–103
    [Google Scholar]
  141. 141. 
    Cockcroft DW. 1983. Mechanism of perennial allergic asthma. Lancet 322:8344253–56
    [Google Scholar]
  142. 142. 
    Darrow LA, Hess J, Rogers CA, Tolbert PE, Klein M, Sarnat SE. 2012. Ambient pollen concentrations and emergency department visits for asthma and wheeze. J. Allergy Clin. Immunol. 130:3630–38.e4
    [Google Scholar]
  143. 143. 
    Erbas B, Akram M, Dharmage SC, Tham R, Dennekamp M et al. 2012. The role of seasonal grass pollen on childhood asthma emergency department presentations. Clin. Exp. Allergy 42:5799–805
    [Google Scholar]
  144. 144. 
    Erbas B, Jazayeri M, Lambert KA, Katelaris CH, Prendergast LA et al. 2018. Outdoor pollen is a trigger of child and adolescent asthma emergency department presentations: a systematic review and meta-analysis. Allergy 73:81632–41
    [Google Scholar]
  145. 145. 
    Erbas B, Chang J-H, Dharmage S, Ong EK, Hyndman R et al. 2007. Do levels of airborne grass pollen influence asthma hospital admissions?. Clin. Exp. Allergy 37:111641–47
    [Google Scholar]
  146. 146. 
    Im W, Schneider D. 2005. Effect of weed pollen on children's hospital admissions for asthma during the fall season. Arch. Environ. Occup. Health 60:5257–65
    [Google Scholar]
  147. 147. 
    Chen C-H, Xirasagar S, Lin H-C. 2006. Seasonality in adult asthma admissions, air pollutant levels, and climate: a population-based study. J. Asthma 43:4287–92
    [Google Scholar]
  148. 148. 
    D'Amato G. 2002. Environmental urban factors (air pollution and allergens) and the rising trends in allergic respiratory diseases. Allergy 57:S7230–33
    [Google Scholar]
  149. 149. 
    Shea KM, Truckner RT, Weber RW, Peden DB. 2008. Climate change and allergic disease. J. Allergy Clin. Immunol. 122:3443–53
    [Google Scholar]
  150. 150. 
    Zanolin ME, Pattaro C, Corsico A, Bugiani M, Carrozzi L et al. 2004. The role of climate on the geographic variability of asthma, allergic rhinitis and respiratory symptoms: results from the Italian study of asthma in young adults. Allergy 59:3306–14
    [Google Scholar]
  151. 151. 
    Hamaoui-Laguel L, Vautard R, Liu L, Solmon F, Viovy N et al. 2015. Effects of climate change and seed dispersal on airborne ragweed pollen loads in Europe. Nat. Clim. Change 5:8766–71
    [Google Scholar]
  152. 152. 
    Lake IR, Jones NR, Agnew M, Goodess CM, Giorgi F et al. 2017. Climate change and future pollen allergy in Europe. Environ. Health Perspect. 125:3385–91
    [Google Scholar]
  153. 153. 
    Neumann JE, Anenberg SC, Weinberger KR, Amend M, Gulati S et al. 2019. Estimates of present and future asthma emergency department visits associated with exposure to oak, birch, and grass pollen in the United States. GeoHealth 3:111–27
    [Google Scholar]
  154. 154. 
    Zhang Y, Isukapalli SS, Bielory L, Georgopoulos PG. 2013. Bayesian analysis of climate change effects on observed and projected airborne levels of birch pollen. Atmos. Environ. 68:64–73
    [Google Scholar]
  155. 155. 
    Barber D, de la Torre F, Feo F, Florido F, Guardia P et al. 2008. Understanding patient sensitization profiles in complex pollen areas: a molecular epidemiological study. Allergy 63:111550–58
    [Google Scholar]
  156. 156. 
    Kmenta M, Zetter R, Berger U, Bastl K. 2016. Pollen information consumption as an indicator of pollen allergy burden. Wien. klin. Wochenschr. 128:1–259–67
    [Google Scholar]
  157. 157. 
    Skjøth CA, Šikoparija B, Jäger S, EAN-Netw. ; 2013. Pollen sources. Allergenic Pollen M Sofiev, K-C Bergmann 9–27 Dordrecht, Neth: Springer
    [Google Scholar]
  158. 158. 
    Scheifinger H, Belmonte J, Buters J, Celenk S, Damialis A et al. 2013. Monitoring, modelling and forecasting of the pollen season. Allergenic Pollen M Sofiev, K-C Bergmann 71–126 Dordrecht, Neth: Springer
    [Google Scholar]
  159. 159. 
    Noh YM, Lee H, Mueller D, Lee K, Shin D et al. 2013. Investigation of the diurnal pattern of the vertical distribution of pollen in the lower troposphere using LIDAR. Atmos. Chem. Phys. 13:157619–29
    [Google Scholar]
  160. 160. 
    Sicard M, Izquierdo R, Alarcón M, Belmonte J, Comerón A, Baldasano JM. 2016. Near-surface and columnar measurements with a micro pulse lidar of atmospheric pollen in Barcelona, Spain. Atmos. Chem. Phys. 16:116805–21
    [Google Scholar]
  161. 161. 
    González-Naharro R, Quirós E, Fernández-Rodríguez S, Silva-Palacios I, Maya-Manzano JM et al. 2019. Relationship of NDVI and oak (Quercus) pollen including a predictive model in the SW Mediterranean region. Sci. Total Environ. 676:407–19
    [Google Scholar]
  162. 162. 
    Hogda KA, Karlsen SR, Solheim I, Tommervik H, Ramfjord H. 2002. The start dates of birch pollen seasons in Fennoscandia studied by NOAA AVHRR NDVI data. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium New York: IEEE
    [Google Scholar]
  163. 163. 
    Hmimina G, Dufrêne E, Pontailler J-Y, Delpierre N, Aubinet M et al. 2013. Evaluation of the potential of MODIS satellite data to predict vegetation phenology in different biomes: an investigation using ground-based NDVI measurements. Remote Sens. Environ. 132:145–58
    [Google Scholar]
  164. 164. 
    Butterfield HS, Malmström CM. 2009. The effects of phenology on indirect measures of aboveground biomass in annual grasses. Int. J. Remote Sens. 30:123133–46
    [Google Scholar]
  165. 165. 
    Soudani K, Hmimina G, Delpierre N, Pontailler J-Y, Aubinet M et al. 2012. Ground-based network of NDVI measurements for tracking temporal dynamics of canopy structure and vegetation phenology in different biomes. Remote Sens. Environ. 123:234–45
    [Google Scholar]
  166. 166. 
    Yan E, Wang G, Lin H, Xia C, Sun H. 2015. Phenology-based classification of vegetation cover types in Northeast China using MODIS NDVI and EVI time series. Int. J. Remote Sens. 36:2489–512
    [Google Scholar]
  167. 167. 
    Feilhauer H, He KS, Rocchini D. 2012. Modeling species distribution using niche-based proxies derived from composite bioclimatic variables and MODIS NDVI. Remote Sens 4:72057–75
    [Google Scholar]
  168. 168. 
    Devadas R, Huete AR, Vicendese D, Erbas B, Beggs PJ et al. 2018. Dynamic ecological observations from satellites inform aerobiology of allergenic grass pollen. Sci. Total Environ. 633:441–51
    [Google Scholar]
  169. 169. 
    Hall J, Lo F, Saha S, Vaidyanathan A, Hess J. 2020. Internet searches offer insight into early-season pollen patterns in observation-free zones. Sci. Rep. 10:11334
    [Google Scholar]
  170. 170. 
    Kloog I, Chudnovsky AA, Just AC, Nordio F, Koutrakis P et al. 2014. A new hybrid spatio-temporal model for estimating daily multi-year PM2.5 concentrations across northeastern USA using high resolution aerosol optical depth data. Atmos. Environ. 95:581–90
    [Google Scholar]
  171. 171. 
    Xiao Q, Chang HH, Geng G, Liu Y. 2018. An ensemble machine-learning model to predict historical PM2.5 concentrations in China from satellite data. Environ. Sci. Technol. 52:2213260–69
    [Google Scholar]
  172. 172. 
    Lyapustin A, Wang Y, Korkin S, Huang D. 2018. MODIS Collection 6 MAIAC algorithm. Atmos. Meas. Tech. 11:105741–65
    [Google Scholar]
  173. 173. 
    Chudnovsky AA, Kostinski A, Lyapustin A, Koutrakis P. 2013. Spatial scales of pollution from variable resolution satellite imaging. Environ. Pollut. 172:131–38
    [Google Scholar]
  174. 174. 
    Liang F, Xiao Q, Wang Y, Lyapustin A, Li G et al. 2018. MAIAC-based long-term spatiotemporal trends of PM2.5 in Beijing, China. Sci. Total Environ. 616–17:1589–98
    [Google Scholar]
  175. 175. 
    Vu BN, Sánchez O, Bi J, Xiao Q, Hansel NN et al. 2019. Developing an advanced PM2.5 exposure model in Lima, Peru. Remote Sens. 11:6641
    [Google Scholar]
  176. 176. 
    Ma Z, Hu X, Sayer AM, Levy R, Zhang Q et al. 2016. Satellite-based spatiotemporal trends in PM2.5 concentrations: China, 2004–2013. Environ. Health Perspect. 124:2184–92
    [Google Scholar]
  177. 177. 
    Belle JH, Chang HH, Wang Y, Hu X, Lyapustin A, Liu Y. 2017. The potential impact of satellite-retrieved cloud parameters on ground-level PM2.5 mass and composition. Int. J. Environ. Res. Public Health 14:101244
    [Google Scholar]
  178. 178. 
    Belle JH, Liu Y. 2016. Evaluation of aqua MODIS collection 6 AOD parameters for air quality research over the continental United States. Remote Sens 8:10815
    [Google Scholar]
  179. 179. 
    Kloog I, Koutrakis P, Coull BA, Lee HJ, Schwartz J. 2011. Assessing temporally and spatially resolved PM2.5 exposures for epidemiological studies using satellite aerosol optical depth measurements. Atmos. Environ. 45:356267–75
    [Google Scholar]
  180. 180. 
    Di Q, Koutrakis P, Schwartz J. 2016. A hybrid prediction model for PM2.5 mass and components using a chemical transport model and land use regression. Atmos. Environ. 131:390–99
    [Google Scholar]
  181. 181. 
    Bi J, Belle JH, Wang Y, Lyapustin AI, Wildani A, Liu Y. 2019. Impacts of snow and cloud covers on satellite-derived PM2.5 levels. Remote Sens. Environ. 221:665–74
    [Google Scholar]
  182. 182. 
    Choi M, Kim J, Lee J, Kim M, Park Y-J et al. 2016. GOCI Yonsei Aerosol Retrieval (YAER) algorithm and validation during the DRAGON-NE Asia 2012 campaign. Atmos. Meas. Tech. 9:31377–98
    [Google Scholar]
  183. 183. 
    Liu Y, Paciorek CJ, Koutrakis P. 2009. Estimating regional spatial and temporal variability of PM2.5 concentrations using satellite data, meteorology, and land use information. Environ. Health Perspect. 117:6886–92
    [Google Scholar]
  184. 184. 
    Xu J-W, Martin R, Donkelaar A, Kim J, Choi M et al. 2015. Estimating ground-level PM2.5 in Eastern China using aerosol optical depth determined from the GOCI Satellite Instrument. Atmos. Chem. Phys. 15:13133–44
    [Google Scholar]
  185. 185. 
    Nizar S, Dodamani BM. 2020. Satellite-based top-down Lagrangian approach to quantify aerosol emissions over California. Q. J. R. Meteorol. Soc. 146:7291626–35
    [Google Scholar]
  186. 186. 
    Al-Hamdan MZ, Crosson WL, Limaye AS, Rickman DL, Quattrochi DA et al. 2009. Methods for characterizing fine particulate matter using ground observations and remotely sensed data: potential use for environmental public health surveillance. J. Air Waste Manag. Assoc. 59:7865–81
    [Google Scholar]
  187. 187. 
    Al-Hamdan MZ, Crosson WL, Economou SA, Estes MGJ, Estes SM et al. 2014. Environmental public health applications using remotely sensed data. Geocarto Int 29:185–98
    [Google Scholar]
  188. 188. 
    Goldberg DL, Lu Z, Streets DG, de Foy B, Griffin D et al. 2019. Enhanced capabilities of TROPOMI NO2: estimating NOX from North American cities and power plants. Environ. Sci. Technol. 53:2112594–601
    [Google Scholar]
  189. 189. 
    Veefkind JP, Aben I, McMullan K, Förster H, de Vries J et al. 2012. TROPOMI on the ESA Sentinel-5 Precursor: a GMES mission for global observations of the atmospheric composition for climate, air quality and ozone layer applications. Remote Sens. Environ. 120:70–83
    [Google Scholar]
  190. 190. 
    Eskes H, van Geffen J, Sneep M, Apituley A, Veefkind JP. 2019. Sentinel-5 precursor/TROPOMI level 2 product user manual nitrogendioxide User Manual, R. Neth. Meteorol. Inst. De Bilt, Neth:.
  191. 191. 
    Kim J, Kim M, Choi M 2017. Monitoring aerosol properties in East Asia from geostationary orbit: GOCI, MI and GEMS. Air Pollution in Eastern Asia: An Integrated Perspective I Bouarar, X Wang, GP Brasseur 323–33 Cham, Switz: Springer Int.
    [Google Scholar]
  192. 192. 
    Zoogman P, Liu X, Suleiman RM, Pennington WF, Flittner DE et al. 2017. Tropospheric emissions: monitoring of pollution (TEMPO). J. Quant. Spectrosc. Radiat. Transf. 186:17–39
    [Google Scholar]
  193. 193. 
    Ingmann P, Veihelmann B, Langen J, Lamarre D, Stark H, Courrèges-Lacoste GB. 2012. Requirements for the GMES Atmosphere Service and ESA's implementation concept: Sentinels-4/-5 and -5p. Remote Sens. Environ. 120:58–69
    [Google Scholar]
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