Assessment of the Impact of Biogenic VOC Emissions in a High Ozone Episode via Integrated Remote Sensing and the CMAQ Model

doi:10.1007/s11707-009-0019-3

Front Earth Sci Chin

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Issue () : 182-197 https://doi.org/10.1007/s11707-009-0019-3

RESEARCH ARTICLE

Assessment of the Impact of Biogenic VOC Emissions in a High Ozone Episode via Integrated Remote Sensing and the CMAQ Model

Kaiyu Cheng¹, Nibin Chang²(

)

1. Environmental Laboratory, SGS Taiwan LTD, Taipei, China; 2. Department of Civil and Environmental Engineering, University of Central Florida, Orlando, FL 32816, USA

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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, 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.

Keywords ozone biogenic emissions volatile organic compounds remote sensing air quality modeling air pollution

Corresponding Author(s): Chang Nibin,Email:nchang@mail.ucf.edu

Issue Date: 05 June 2009

Cite this article:

Kaiyu Cheng,Nibin Chang. Assessment of the Impact of Biogenic VOC Emissions in a High Ozone Episode via Integrated Remote Sensing and the CMAQ Model[J]. Front Earth Sci Chin, 0, (): 182-197.

URL:

https://academic.hep.com.cn/fesci/EN/10.1007/s11707-009-0019-3
https://academic.hep.com.cn/fesci/EN/Y0/V/I/182

Fig.1 Modeling system and related data processing ()

Fig.2 Structure between input data processors and CCTM

Fig.3 Preparation of land use data for SMOKE

Fig.4 Process of LULC classification analysis using SPOT imageries

Fig.5 Procedure of estimation of biogenic gas emissions

Fig.6 Modeling domains for the Models-3/CMAQ analysis

Fig.7 Nested structure in relation to the meteorological forecasting analysis

Tab.1 Type 1 classification of land use and land cover in Taiwan

Tab.2 Type 2 classification of land use and land cover in Taiwan

Fig.8 Results of image-based LULC classification

Fig.9 Results of biogenic gas emissions

Tab.3 Emissions from anthropogenic sources with 1 km resolution/(tonnes/day)

Tab.4 Emissions from biogenic sources with 1 km resolution/ (tonnes/day)

Fig.10

Fig.11 Gridded SMOKE emission output data and time series of

Fig.12

Fig.13

Fig.14

Fig.15 MM5 outputs for CMAQ simulation analysis

Fig.16 10/25 04:00 wind and temperature field (1 km grid)

Fig.17 Base case results for ground level O at 1 km grid spacing on October 25, 2001, at (a) 7:00 local time, and (b) 13:00 local time

Fig.18 Scenario I results of ground level O concentrations at 1 km grid spacing (a) 13:00 local time on October 25, 2001, and (b) 0:00 local time on October 26, 2001.

Fig.19 Change in ground level O3 (base case minus Scenario I) at 1 km grid spacing on October 26, 2001, at local time (a) 14:00, and (b) 0:00

Fig.20 Change in ground level O concentrations (base case minus Scenario II) at 1 km grid spacing on October 26, 2001, at local time (a) 15:00, and (b) 5:00.

Fig.21 Simulation outcome at four regional air quality monitoring stations

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