We developed a dispersion model (RapidAir^®) to estimate air pollution concentrations at fine spatial resolution over large geographical areas with fast run times. Concentrations were modelled at 5 m spatial resolution over an area of ∼3500 km² in <10 min. RapidAir^® was evaluated by estimating NO_x and NO₂ concentrations at 86 continuous monitoring sites in London, UK during 2008. The model predictions explained 66% of the spatial variation (r = 0.81) in annual NO_x concentrations observed at the monitoring sites. We included discrete canyon models or geospatial surrogates (sky view factor, hill shading and wind effect) to improve the accuracy of model predictions at kerbside locations. Geospatial surrogates provide alternatives to discrete street canyon models where it is impractical to run canyon models for thousands of streets within a large city dispersion model (with advantages including: ease of operation; faster run times; and more complete treatment of building effects).

我们开发了一个扩散模型（的RapidAir ^®）在精细空间分辨了快速的运行时间的大地理区域估计空气污染浓度。在小于10分钟的时间内，以3500 km ²的面积以5 m的空间分辨率模拟了浓度。的RapidAir ^®通过估计NO评价_X和NO _2种在2008年期间在英国伦敦86个连续监测点浓度的模型预测解释的空间变化的66％的年NO（R = 0.81）_X在监测现场观察到的浓度。我们添加了离散的峡谷模型或地理空间替代模型（天空因子，山体阴影和风影响）以提高路边位置模型预测的准确性。地理空间替代方案为离散的街道峡谷模型提供了替代方案，因为在大型城市分散模型中为数千条街道运行峡谷模型是不切实际的（优点包括：易于操作；运行时间更快；对建筑效果的处理更加完整）。