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首页> 外文期刊>Journal of Environmental Management >Estimation of biogenic emissions with satellite-derived land use and land cover data for air quality modeling of Houston-Galveston ozone nonattainment area
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Estimation of biogenic emissions with satellite-derived land use and land cover data for air quality modeling of Houston-Galveston ozone nonattainment area

机译：利用卫星衍生的土地利用和土地覆盖数据估算生物发生排放量，用于休斯顿-加尔维斯顿臭氧不达标区域的空气质量模拟

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The Houston-Galveston Area (HGA) is one of the most severe ozone non-attainment regions in the US. To study the effectiveness of controlling anthropogenic emissions to mitigate regional ozone nonattainment problems, it is necessary to utilize adequate datasets describing the environmental conditions that influence the photochemical reactivity of the ambient atmosphere. Compared to the anthropogenic emissions from point and mobile sources, there are large uncertainties in the locations and amounts of biogenic emissions. For regional air quality modeling applications, biogenic emissions are not directly measured but are usually estimated with meteorological data such as photo-synthetically active solar radiation, surface temperature, land type, and vegetation database. In this paper, we characterize these meteorological input parameters and two different land use land cover datasets available for HGA: the conventional biogenic vegetation/land use data and satellite-derived high-resolution land cover data. We describe the procedures used for the esti mation of biogenic emissions with the satellite derived land cover data and leaf mass density information. Air quality model simulations were performed using both the original and the new biogenic emissions estimates. The results showed that there were considerable uncertainties in biogenic emissions inputs. Subsequently, ozone predictions were affected up to 10 ppb, but the magnitudes and locations of peak ozone varied each day depending on the upwind or downwind positions of the biogenic emission sources relative to the anthropogenic NO_x and VOC sources. Although the assessment had limitations such as heterogeneity in the spatial resolutions, the study highlighted the significance of biogenic emissions uncertainty on air quality predictions. However, the study did not allow extrapolation of the directional changes in air quality corresponding to the changes in LULC because the two datasets were based on vastly different LULC category definitions and uncertainties in the vegetation distributions.
机译：休斯顿-加尔维斯顿地区（HGA）是美国最严重的臭氧不达标地区之一。为了研究控制人为排放以减轻区域性臭氧不达标问题的有效性，有必要利用描述影响环境大气光化学反应性的环境条件的适当数据集。与点源和移动源的人为排放相比，生物源排放的位置和数量存在很大的不确定性。对于区域空气质量建模应用，不是直接测量生物排放，而是通常使用气象数据（例如光合有效太阳辐射，地表温度，土地类型和植被数据库）进行估算。在本文中，我们表征了这些气象输入参数和可用于HGA的两个不同的土地利用土地覆盖数据集：常规生物成因植被/土地利用数据和卫星衍生的高分辨率土地覆盖数据。我们用卫星得出的土地覆盖数据和叶片质量密度信息描述了估算生物成因排放的程序。使用原始和新的生物排放估算值进行了空气质量模型模拟。结果表明，生物排放量输入存在很大的不确定性。随后，臭氧的预测值受到影响，最高可达10 ppb，但是臭氧峰值的强度和位置每天都在变化，具体取决于生物排放源相对于人为NO_x和VOC源的上风或下风位置。尽管评估存在空间分辨率异质性等局限性，但该研究强调了生物排放不确定性对空气质量预测的重要性。但是，该研究不允许推断与LULC变化相对应的空气质量方向变化，因为这两个数据集基于极为不同的LULC类别定义和植被分布的不确定性。

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