Abstract
In many metropolitan regions, natural sources contribute a substantial fraction of volatile organic compound (VOC) emissions. These biogenic VOC emissions are precursors to tropospheric Ozone (O3) formation. Because forests make up 59% of the land area in Taiwan Province, China, the biogenic VOC emissions from forests and farmland could play an important role in photochemical reactions. On the other hand, anthropogenic emissions might also be one of the major inputs for ground level O3 concentrations. Hence, emission inventory data, grouped as point, area, mobile and biogenic VOC sources, are a composite of reported and estimated pollutant emission information and are used by many air quality models to simulate ground level O3 concentrations. Before using relevant air quality models, the emission inventory data generally require huge amounts of processing for spatial, temporal, and species congruence with respect to the associated air quality modeling work. The fist part of this research applied satellite remote sensing and geographic information system (GIS) analyses to characterize land use/land cover (LULC) patterns, integrating various sources of anthropogenic emissions and biogenic emissions associated with a variety of plant species. To investigate the significance of biogenic VOC emissions on ozone formation, meteorological and air quality modeling were then employed to generate hourly ozone estimates for a case study of a high ozone episode in southern Taiwan, which is the leading industrial hub on the island. To enhance the modeling accuracy, a unique software module, SMOKE, was set up for emission processing to prepare emission inputs for the U.S. EPA’s Models-3/CMAQ. An emission inventory of Taiwan, TEDS 4.2, was used as the anthropogenic emission inventory. Biogenic emission modeling was accomplished by BEIS-2 in SMOKE, with improvement of local LULC data and revised emission factors. Research findings show that the majority of biogenic VOC emissions occur in the mountainous areas and farmlands. However, the modeling outputs show that downwind of the most heavily populated and industrialized areas, these biogenic VOC emissions have less impact on air quality than do anthropogenic emissions.
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References
Benjamin M T, Sudol M, Bloch L, Winer A M (1996). Low-emitting urban forests: a taxonomic methodology for assigning isoprene and monoterpene emission rates. Atmospheric Environment, 30(9): 1437–1452(16)
Byun D W, Ching J (1999). Science algorithms of the EPA Model-3 community multiscale air quality (CMAQ) modeling system. Research Triangle Park (NC): EPA/600/R-99/030, National Exposure Research Laboratory, U.S. EPA
Cheng D F, Chang G H (1999). The Estimation of Biogenic Emission Inventory with Model. Proceeding 16th Air Pollution Control Technology Conference, Taipei: 1999
Colella P, Woodward P (1987). The piecewise parabolic method (PPM) for gas-dynamical simulations. J Comp Phys, 54, 174–201
Guenther A, Geron C, Pierce T, Lamb B, Harley P, Fall R (2000). Natural emissions of non-methane volatile organic compounds; carbon monoxide, and oxides of Nitrogen from North America. Atmospheric Environment, 34: 2205–2230
Guenther A, Zimmerman P, Harley P, Momson R, Fall R (1993). Isoprene and monoterpene emission rate variability: model evaluation and sensitivity analysis. J Geophys Res, 98: 12609–12791
MCNC (2000). Smoke User Manual, Version 1.4. MCNC-Environmental Modeling Center, Research Triangle Park, NC, USA
Ning S K, Chang N B, Jeng K Y, Tseng Y H (2006). Soil erosion and non-point sources pollution impacts assessment with the aid of remote sensing. Journal of Environmental Management, 79(1): 88–101, 2006
U.S. EPA (1999). User Manual for the EPAThird-Generation Air Quality Modeling System (Models-3 Version 3.0), EPA-600/R-99/055, USEPA/ORD/ NERL/AMD, June 1999
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Cheng, K., Chang, NB. Assessment of the impact of biogenic VOC emissions in a high ozone episode via integrated remote sensing and the CMAQ model. Front. Earth Sci. China 3, 182–197 (2009). https://doi.org/10.1007/s11707-009-0019-3
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DOI: https://doi.org/10.1007/s11707-009-0019-3