Huang Ling, McDonald-Buller Elena, McGaughey Gary, Kimura Yosuke, Allen David T
a Center for Energy and Environmental Resources , University of Texas at Austin , Austin , TX , USA.
J Air Waste Manag Assoc. 2015 Oct;65(10):1194-205. doi: 10.1080/10962247.2015.1057302.
Accurate estimates of biogenic emissions are required for air quality models that support the development of air quality management plans and attainment demonstrations. Land cover characterization is an essential driving input for most biogenic emissions models. This work contrasted the global Moderate Resolution Imaging Spectroradiometer (MODIS) land cover product against a regional land cover product developed for the Texas Commissions on Environmental Quality (TCEQ) over four climate regions in eastern Texas, where biogenic emissions comprise a large fraction of the total inventory of volatile organic compounds (VOCs) and land cover is highly diverse. The Model of Emissions of Gases and Aerosols from Nature (MEGAN) was utilized to investigate the influences of land cover characterization on modeled isoprene and monoterpene emissions through changes in the standard emission potential and emission activity factor, both separately and simultaneously. In Central Texas, forest coverage was significantly lower in the MODIS land cover product relative to the TCEQ data, which resulted in substantially lower estimates of isoprene and monoterpene emissions by as much as 90%. Differences in predicted isoprene and monoterpene emissions associated with variability in land cover characterization were primarily caused by differences in the standard emission potential, which is dependent on plant functional type. Photochemical modeling was conducted to investigate the effects of differences in estimated biogenic emissions associated with land cover characterization on predicted ozone concentrations using the Comprehensive Air Quality Model with Extensions (CAMx). Mean differences in maximum daily average 8-hour (MDA8) ozone concentrations were 2 to 6 ppb with maximum differences exceeding 20 ppb. Continued focus should be on reducing uncertainties in the representation of land cover through field validation.
Uncertainties in the estimation of biogenic emissions associated with the characterization of land cover in global and regional data products were examined in eastern Texas. Misclassification between trees and low-growing vegetation in central Texas resulted in substantial differences in isoprene and monoterpene emission estimates and predicted ground-level ozone concentrations. Results from this study indicate the importance of land cover validation at regional scales.
支持空气质量管控计划制定和达标论证的空气质量模型需要对生物源排放进行准确估算。土地覆盖特征描述是大多数生物源排放模型的重要驱动输入。本研究将全球中分辨率成像光谱仪（MODIS）土地覆盖产品与为德克萨斯州环境质量委员会（TCEQ）开发的区域土地覆盖产品进行了对比，研究区域为德克萨斯州东部的四个气候区，该地区生物源排放占挥发性有机化合物（VOCs）总清单的很大一部分，且土地覆盖高度多样化。利用自然气体和气溶胶排放模型（MEGAN），通过分别和同时改变标准排放潜力和排放活动因子，研究土地覆盖特征描述对模拟异戊二烯和单萜烯排放的影响。在德克萨斯州中部，MODIS土地覆盖产品中的森林覆盖率显著低于TCEQ数据，这导致异戊二烯和单萜烯排放量的估算值大幅降低，降幅高达90%。与土地覆盖特征变化相关的预测异戊二烯和单萜烯排放量差异主要是由标准排放潜力的差异造成的，而标准排放潜力取决于植物功能类型。利用扩展综合空气质量模型（CAMx）进行光化学建模，以研究与土地覆盖特征描述相关的生物源排放估算差异对预测臭氧浓度的影响。最大日平均8小时（MDA8）臭氧浓度的平均差异为2至6 ppb，最大差异超过20 ppb。应持续关注通过实地验证减少土地覆盖表征中的不确定性。
在德克萨斯州东部，研究了全球和区域数据产品中与土地覆盖特征描述相关的生物源排放估算的不确定性。德克萨斯州中部树木和低生长植被的误分类导致异戊二烯和单萜烯排放估算以及预测的地面臭氧浓度存在显著差异。本研究结果表明了区域尺度土地覆盖验证的重要性。
