IMPROVEMENTS IN AIR QUALITY MODELLING BY USING SURFACE BOUNDARY LAYER PARAMETERS DERIVED FROM SATELLITE LAND COVER DATA

摘要

Many atmospheric dispersion models include only simple treatment of surface features to estimate the wind profiles and stability parameters. Detailed characterisation of the land cover, particularly in large and complex urban conurbations, is especially important, as the surface features can vary significantly over the area. This paper discusses the use of satellite land cover data to derive spatially resolved surface boundary layer (SBL) parameters. These parameters have been used in an air quality model, PEARL (Prediction Air Quality in Urban and Regional Locations) for estimating monthly and annual CO concentrations. Land cover data, derived from LANDSAT Thematic Mapper Imagery, has been used to estimate SBL parameters (surface roughness length, albeedo, Bowen ratio and anthropogenic heat flux) for a study area of 10000 km~2 encompassing Greater London and the surrounding counties. The SBL parameters have been assigned according to major land cover types for the whole area at a spatial resolution of 1 x 1 km. Predictions from two versions of the PEARL model (one with land cover data and one without) have been compared with each other and with measured data for annual and monthly CO concentrations from seven London air quality monitoring sites. This comparison shows that differences between predicted and observed values can be reduced by up to a factor of three. The use of SBL parameters derived from land cover data also yields more detailed predicted annual CO spatial patterns especially in and around suburban areas. The performance of both versions of the model for monthly CO concentrations has been compared with a range of statistical measures. This comparison confirms that improved agreement is observed between modelled and measured monthly CO concentrations when use is made of spatially resolved SBL parameters.