ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa


Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2021, Vol. 18, No. 2, pp. 97-111

Space observations of surface parameters for AERMOD modeling of industrial air pollution. Part 1. Literature review, data, land use classification

B.M. Balter 1 , D.B. Balter 1 , V.V. Egorov 1 , M.V. Stalnaya 1 , M.V. Faminskaya 2 
1 Space Research Institute RAS, Moscow, Russia
2 Russian State Social University, Moscow, Russia
Accepted: 09.12.2020
DOI: 10.21046/2070-7401-2021-18-2-97-111
In the work, the AERMOD industrial pollution dispersion model is enhanced with remote sensing observations and climatic models based on them. The focus is on the three surface parameters (albedo, roughness, Bowen ratio) and on land use classification on which it depends. We model maximum hourly concentrations and the resulting acute health risk and assess the effect produced by using remote sensing data for local areas around industrial plants instead of global standard AERMOD parameters. In this part of the publication, we review the research on extracting the AERMOD-related surface parameters from space remote sensing data and the published data about the effect of their usage on model concentrations. Then, the data we used in our research of this problem (five real multi-source plants) and the approach to measuring the effect of remote sensing data are described. The effect on each plant’s critical pollutant is measured in three ways: a) as difference between the yearly maxima of hourly concentrations of a critical pollutant (“absolute”); b) the same limited to daytime workhours and 95 % quantile instead of absolute maximum (“regulatory”); c) as maximum hourly difference over a year (“instant”). The measure of effect is divided either by the reference concentration of the pollutant, which yields the impact on health risk, or by the concentration obtained with AERMOD standards, which yields a relative measure of impact. In this part of the publication, we focus on the effect of using remote sensing for land use classification. It is considerable for all three criteria used a)–c).
Keywords: AERMOD, pollutant dispersion model, albedo, roughness, Bowen parameter, land use classification, maximal hourly concentrations, Landsat, ALOS, GLASS, ERA5
Full text


  1. Balter B., Balter D., Egorov V., Stalnaya M., Faminskaya M., Landsat Land Use Classification for Assessing Health Risk from Industrial Air Pollution, Izvestiya, Atmospheric and Oceanic Physics, 2018, Vol. 54, No. 9, pp. 1334–1340.
  2. Balter B. M., Egorov V. V., Kottsov V. A., Faminskaya M. V., Recognition of Earth surface categories based on correlation portraits and its use in modeling atmospheric pollution dispersion, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2019, Vol. 16, No. 2, pp. 29–41 (in Russian).
  3. Human Health Risk Assessment from Environmental Chemicals, Rospotrebnadzor Guidelines No., Moscow, 2004, 144 p. (in Russian).
  4. AERSURFACE User’s Guide, US EPA Research Triangle Park, 2008, 104 p.
  5. Arsanjani J. J., Characterizing and Monitoring Global Landscapes Using GlobeLand30 Datasets: The First Decade of the Twenty-first Century, Intern. J. Digital Earth, 2018, Vol. 12, pp. 642–660, DOI: 10.1080/17538947.2018.1470689.
  6. Arsanjani J. J., See L., Tayyebi A., Assessing the Suitability of GlobeLand30 for Mapping Land Cover in Germany, Intern. J. Digital Earth, 2016, Vol. 9, pp. 873–891, DOI: 10.1080/17538947.2016.1151956.
  7. Baldinelli G., Bonafoni S., Rotili A., Albedo Retrieval from Multispectral Landsat 8 Observation in Urban Environment: Algorithm Validation by in situ Measurements, IEEE J. Selected Topics in Applied Earth Observations and Remote Sensing, 2017, Vol. 10, pp. 4504–4511, DOI: 10.1109/JSTARS.2017.2721549.
  8. Balter B., Faminskaya M., Irregularly Emitting Air Pollution Sources: Acute Health Risk Assessment Using AERMOD and the Monte Carlo Approach to Emission Rate, Air Quality, Atmosphere and Health, 2017, Vol. 10, pp. 401–409.
  9. Bo X., Wang G., Tian J., Yang J., Gao X., Huang Y., Li S., Standard Systems of Surface Parameters in AERMOD, China Environmental Science, 2015, Vol. 35(9), pp. 2570–2575 (in Chinese).
  10. Cho J., Miyazaki S., Yeh P. J. F., Kim W., Kanae S., Oki T., Testing the Hypothesis on the Relationship Between Aerodynamic Roughness Length and Albedo using Vegetation Structure Parameters, Intern. J. Biometeorology, 2012, Vol. 56(2), pp. 411–418, DOI: 10.1007/s00484-011-0445-2.
  11. Faulkner W., Shaw B. W., Grosch T., Sensitivity of Two Dispersion Models (AERMOD and ISCST3) to Input Parameters for a Rural Ground-Level Area Source, J. Air and Waste Management Association, 2008, Vol. 58(10), pp. 1288–1296, DOI: 10.3155/1047-3289.58.10.1288.
  12. Garcia-Mora T., Mas J. F., Hinkley E. A., Land Cover Mapping Applications with MODIS: A Literature Review, Intern. J. Digital Earth, 2012, Vol. 5(1), pp. 63–87, DOI: 10.1080/17538947.2011.565080.
  13. Gowda P., Chávez J. L., Howell T. A., Marek T. H., New L. L. New L. L., Surface Energy Balance Based Evapotranspiration Mapping in the Texas High Plains, Sensors, 2008, Vol. 8, pp. 5186–5201, DOI: 10.3390/s8085186.
  14. Grosch T., Lee R. F., Sensitivity of the AERMOD Air Quality Model to the Selection of Land Use Parameters, Trans. Ecology and the Environment, 1999, Vol. 29, pp. 803–812.
  15. Gupta R., Prasad T. S., Vijayan D., Estimation of Roughness Length and Sensible Heat Flux from WiFS and NOAA AVHRR Data, Advance in Space Research, 2002, Vol. 29(1), pp. 33–38.
  16. He T., Wang D., Qu Y., Land Surface Albedo, Comprehensive Remote Sensing. Vol. 5: Earth’s Energy Budget, S. Liang (ed.), Amsterdam: Elsevier, 2018, pp. 140–162.
  17. Isakov V., Venkatram A., Touma J. S., Koracin D., Otte T. L., Evaluating the Use of Outputs from Comprehensive Meteorological Models in Air Quality Modeling Applications, Atmospheric Environment, 2007, Vol. 41, pp. 1689–1705.
  18. Jiang B., Liang S., Jia A., Xu J., Zhang X., Xiao Z., Zhao X., Jia K., Yao Y., Validation of the Surface Daytime Net Radiation Product from Version 4.0 GLASS Product Suite, IEEE Geoscience and Remote Sensing Letters, 2018, Vol. 16(4), pp. 509–513, DOI: 10.1109/LGRS.2018.2877625.
  19. Karvounis G., Deligiorgi D., Philippopoulos K., On the Sensitivity of AERMOD to Surface Parameters under Various Anemological Conditions, Proc. 11th Intern. Conf. Harmonisation within Atmospheric Dispersion Modelling for Regulatory Purposes, D. J. Carruthers, Ch. A. McHugh (eds.), Cambridge Environmental Research Consultants Ltd., 2007, pp. 43–47.
  20. Kesarkar A., Dalvi M., Kaginalkar A., Ojhab A., Coupling of the Weather Research and Forecasting Model with AERMOD for Pollutant Dispersion Modeling. A Case Study for PM10 Dispersion over Pune, India, J. Atmospheric Environment, 2007, Vol. 41(9), pp. 1976–1988.
  21. Kumar A., Dikshit A. K., Fatima S., Patil R. S., Application of WRF Model for Vehicular Pollution Modelling Using AERMOD, Atmospheric and Climate Sciences, 2015, Vol. 5, pp. 57–62.
  22. Kumar A., Patil R. S., Dikshit A. K., Kumar R., Application of WRF Model for Air Quality Modelling and AERMOD — A Survey, Aerosol and Air Quality Research, 2017, Vol. 17, pp. 1925–1937.
  23. Liang S., Zhang X., Xiao Z., Cheng J., Liu Q, Zhao X., Global Land Surface Satellite (GLASS) Products. Algorithms, Validation and Analysis, Cham: Springer, 2014.
  24. Lindberg F., Grimmond C. S. B., Gabey A., Huang B., Kent C. W., Sun T., Theeuwes N. E., Jarvi L., Ward H. C., Capel-Timms I., Chang Y., Jonsson P., Krave N., Liu D., Meyer D., Olofson K. F. G., Tan J., Wastberg D., Xue L., Zhang Z., Urban Multi-scale Environmental Predictor (UMEP): An Integrated Tool for City-based Climate Services, Environmental Modelling and Software, 2018, Vol. 99, pp. 70–87.
  25. Liu J., Schaaf C., Strahler A., Jiao Z., Shuai Y., Zhang Q., Roman M., Augustine J. A., Dutton E. G., Validation of Moderate Resolution Imaging Spectroradiometer (MODIS) Albedo Retrieval Algorithm: Dependence of Albedo on Solar Zenith Angle, J. Geophysical Research, 2009, Vol. 114(D1), D01106, DOI: 10.1029/2008JD009969.
  26. Long G., Cordova J. F., Tanrikulu S., An Analysis of AERMOD Sensitivity to Input Parameters in the San Francisco Bay Area, Proc. 13th Joint Conf. Applications of Air Pollution Meteorology with the Air and Waste Management Association, Pittsburgh, PA: A&WMA, 2004, pp. 203–206.
  27. Malek E., Comparison of the Bowen Ratio-energy Balance and Stability-Corrected Aerodynamic Methods for Measurement of Evapotranspiration, Theoretical and Applied Climatology, 1993, Vol. 48, pp. 167–178.
  28. Pape M., Vohland M., A Comparison of Total Shortwave Surface Albedo Retrievals from MODIS and TM Data, ISPRS TC VII Symp., Vienna, Austria: IAPRS, 2010, Vol. 38, pp. 447–451.
  29. Pascal M., Vondou A. D., Francois M. K., Case Study of Pollutants Concentration Sensitivity to Meteorological Fields and Land Use Parameters over Douala (Cameroon) Using AERMOD Dispersion Model, Atmosphere, 2011, Vol. 2(4), pp. 715–741.
  30. Pongprueksa P., Chatchupong T., High Resolution Land Cover Data for Thailand’s Air Quality Impact Assessment, 5th Intern. Conf. Environmental Engineering, Science and Management, Bangkok, Thailand, 2016, DOI: 10.13140/RG.2.1.4427.8642.
  31. Qu Y., Qiang L., Liang S., Wang L., Liu N., Liu S., Direct-Estimation Algorithm for Mapping Daily Land-Surface Broadband Albedo from MODIS Data, IEEE Trans. Geoscience and Remote Sensing, 2014, Vol. 52(2), pp. 907–919.
  32. Srivastava H., Patel P., Navalgund R. R., Sharma Y., Retrieval of Surface Roughness Using Multi-polarized Envisat-1 ASAR Data, Geocarto Intern., 2008, Vol. 23(1), pp. 67–77, DOI: 10.1080/10106040701538157.
  33. Tadono T., Nagai H., Ishida H., Oda F., Naito S., Minakawa K., Iwamoto H., Generation of the 30 m Mesh Global Digital Surface Model by ALOS PRISM, Intern. Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2016, XLI-B4, pp. 157–162, available at:
  34. Verhoes N., Lievens H., Soil Moisture Retrieval from Synthetic Aperture Radar: Facing the Soil Roughness Parameterization Problem, Remote Sensing of Energy Fluxes and Soil Moisture Content, G. P. Petropoulos (ed.), Boca Raton: CRC Press, 2014, pp. 323–344.
  35. Zhang F., Sha M., Wang G., Li Z., Shao Y., Urban Aerodynamic Roughness Length Mapping Using Multitemporal SAR Data, Advances in Meteorology, 2017, Art. No. 8958926, DOI: 10.1155/2017/8958926.