GEP  Vol.8 No.7 , July 2020
Space Observations and Global Climatic Data Reanalysis in AERMOD Modeling Package to Enhance the Industrial Air Pollution and Health Risk Assessment
Abstract: We try to enhance the AERMOD industrial pollution dispersion model with remote sensing observations and climatic models based on them. In this paper, we focus on surface parameters (albedo, roughness, Bowen ratio) and land use classification on which they depend. We model maximum hourly concentrations and the resulting acute health risk and assess the effect on them produced by using remote sensing data for local areas around industrial plants instead of global standard AERMOD parameters. We consider five real multi-source plants for the effect of classification and two of them for the effect of surface parameters. The effect on the 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 relative measure of impact. For a), the impact of roughness dominates, that of albedo is small and that of the Bowen ratio is almost zero. For b), the impact of roughness is less prominent, and that of albedo and Bowen ratio is noticeable. For c), the impact is considerable for all three parameters. The effect of land use classification is considerable in all three cases a) - c). We provide the figures for different measures of remote sensing data effect and discuss the perspective of using remote sensing data in regulatory context.
Cite this paper: Faminskaya, M. (2020) Space Observations and Global Climatic Data Reanalysis in AERMOD Modeling Package to Enhance the Industrial Air Pollution and Health Risk Assessment. Journal of Geoscience and Environment Protection, 8, 65-83. doi: 10.4236/gep.2020.87004.

[1]   (2008). Aersurface User’s Guide. Research Triangle Park, NC: US Environmental Protection Agency.

[2]   Baldinelli, G., Bonafoni, S., & Rotili, A. (2017). Albedo Retrieval from Multispectral Landsat 8 Observation in Urban Environment: Algorithm Validation by in Situ Measurements. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10, 4504-4511.

[3]   Balter, B., Balter, D., Egorov, V., Stalnaya, M., & Faminskaya, M. (2018). Landsat Land Use Classification for Assessing Health Risk from Industrial Air Pollution. Izvestiya, Atmospheric and Oceanic Physics/Issledovanie Zemli iz kosmosa, 9, 1334-1340.

[4]   Balter, B., & Faminskaya, M. (2017). Irregularly Emitting Air Pollution Sources: Acute Health Risk Assessment Using AERMOD and the Monte Carlo Approach to Emission Rate. Air Quality, Atmosphere & Health, 10, 401-409.

[5]   Bastiaanssen, W., & Roebeling, R. A. (1993). Analysis of Land Surface Exchange Processes in Two Agricultural Regions in Spain Using Thematic Mapper Simulator Data. Exchange Processes at the Land Surface for a Range of Space and Time Scales. In Proceedings of the Yokohama Symposium (pp. 407-416). Yokohama: IAHS Publ. No. 212.

[6]   Bastiaanssen, W., Menenti, M., Feddes, R. A., & Holtslag, A. A. M. (1998a). A Remote Sensing Surface Energy Balance Algorithm for Land (SEBAL) 1. Formulation. Journal of Hydrology, 212-213, 198-212.

[7]   Bastiaanssen, W., Pelgrum, H., Wang, J., Ma, Y., Moreno, J., Roerink, G. J., & van der Wal, T. (1998b). A Remote Sensing Surface Energy Balance Algorithm for Land (SEBAL) 2. Validation. Journal of Hydrology, 212-213, 212-229.

[8]   Bo, X., Wang, G., Tian, J., Yang, J., Gao, X., Huang, Y., & Li, S. (2015). Standard Systems of Surface Parameters in AERMOD. China Environmental Science, 35, 2570-2575. (In Chinese)

[9]   Cho, J., Miyazaki, S., Yeh, P. J. F., Kim, W., Kanae, S., & Oki, T. (2012). Testing the Hypothesis on the Relationship between Aerodynamic Roughness Length and Albedo Using Vegetation Structure Parameters. International Journal of Biometeorology, 56, 411-418.

[10]   Garcia-Mora, T., Mas, J. F., & Hinkley, E. A. (2012). Land Cover Mapping Applications with MODIS: A Literature Review. International Journal of Digital Earth, 5, 63-87.

[11]   Gowda, P, Chávez, J. L., Howell, T. A., Marek, T. H., & New, L. L. (2008). Surface Energy Balance Based Evapotranspiration Mapping in the Texas High Plains. Sensors, 8, 5186-5201.

[12]   Grosch, T., & Lee, R. F. (1999). Sensitivity of the AERMOD Air Quality Model to the Selection of Land Use Parameters. Transactions on Ecology and the Environment, 29, 803-812.

[13]   Gupta, R., Prasad, T. S., & Vijayan, D. (2002). Estimation of Roughness Length and Sensible Heat Flux from WiFS and NOAA AVHRR Data. Advances in Space Research, 29, 33-38.

[14]   He, T., Wang, D., & Qu, Y. (2018). Land Surface Albedo. In S. Liang (Ed.), Comprehensive Remote Sensing. Volume 5: Earth’s Energy Budget (pp. 140-162). Amsterdam: Elsevier.

[15]   Isakov, V., Venkatram, A., Touma, J. S., Koracin, D., & Otte, T. L. (2007). Evaluating the Use of Outputs from Comprehensive Meteorological Models in Air Quality Modeling Applications. Atmospheric Environment, 41, 1689-1705.

[16]   Karvounis, G., Deligiorgi, D., & Philippopoulos, R. (2007). On the Sensitivity of AERMOD to Surface Parameters under Various Anemological Conditions. In Proceedings of the 11th International Conference on Harmonisation within Atmospheric Dispersion Modelling for Regulatory Purposes (pp. 43-47). Cambridge: Cambridge Environmental Research Consultants Ltd.

[17]   Kent, C., Grimmond, S., Gatey, D., & Hirano, K. (2019). Urban Morphology Parameters from Global Digital Elevation Models: Implications for Aerodynamic Roughness and for Wind-Speed Estimation. Remote Sensing of Environment, 221, 316-339.

[18]   Kesarkar, A., Dalvi, M., Kaginalkar, A., & Ojhab, A. (2007). Coupling of the Weather Research and Forecasting Model with AERMOD for Pollutant Dispersion Modeling. A Case Study for PM10 Dispersion over Pune, India. Journal of Atmospheric Environment, 41, 1976-1988.

[19]   Kumar, A., Patil, R. S., Dikshit, A. K., & Kumar, R. (2017). Application of WRF Model for Air Quality Modelling and AERMOD—A Survey. Aerosol and Air Quality Research, 17, 1925-1937.

[20]   Liang, S., Wang, D., He, T., & Yu, Y. (2019). Remote Sensing of Earth’s Energy Budget: Synthesis and Review. International Journal of Digital Earth, 12, 737-780.

[21]   Lindberg, F., Grimmond, C. S. B., & Gabey, A. (2018). Urban Multi-Scale Environmental Predictor (UMEP): An Integrated Tool for City-Based Climate Services. Environmental Modelling & Software, 99, 70-87.

[22]   Malek, E. (1993). Comparison of the Bowen Ratio-Energy Balance and Stability-Corrected Aerodynamic Methods for Measurement of Evapotranspiration. Theoretical and Applied Climatology, 48, 167-178.

[23]   Pape, M., & Vohland, M. (2010). A Comparison of Total Shortwave Surface Albedo Retrievals from MODIS and TM Data. In ISPRS TC VII Symposium (Vol. 38, pp. 447-451). Vienna: IAPRS.

[24]   Pongprueksa, P., & Chatchupong, T. (2016). High Resolution Land Cover Data for Thailand’s Air Quality Impact Assessment. In 5th International Conference on Environmental Engineering, Science and Management (pp. 1-6). Bangkok.

[25]   Simpson, M., Jasinski, M. F., Borak, J., Blonski, S., Spruce, J., Walker, H., & Delle Monache, L. (2012). Integrating NASA Earth Science Capabilities into the Interagency Modeling and Atmospheric Assessment Center for Improvements in Atmospheric Transport and Dispersion Modeling. Lawrence Livermore National Laboratory, LLNL-TR-596732.

[26]   Tadono, T., Nagai, H., Ishida, H., Oda, F., Naito, S., Minakawa, K., & Iwamoto, H. (2016). Generation of the 30 m-Mesh Global Digital Surface Model by ALOS PRISM. In The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B4. XXIII ISPRS Congress (pp. 157-162). Prague.

[27]   Zhao, X., Liang, S., & Liu, S. (2013). The Global Land Surface Satellite (GLASS) Remote Sensing Data Processing System and Products. Remote Sensing, 5, 2436-2450.