Estimation of biogenic emissions with satellite-derived land use and land cover data for air quality modeling of Houston-Galveston ozone nonattainment area

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 estimation 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 NOx 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.