Estimating urban morphometry at the neighborhood scale for improvement in modeling long-term average air pollution concentrations

Land use regression (LUR) models have emerged as useful tools to estimate neighborhood scale variability (100s of meters) in urban air pollutant concentrations. We describe an approach to estimate urban morphometry and, in particular, the street canyon aspect ratio (h/w) of buildings for improvement of land use regression models of traffic related pollution without the requirement for detailed site-specific measurements. Landsat Enhanced Thematic Mapper Plus (ETM+) data and high resolution orthophotos were used to estimate a building height surface, from which an aspect ratio surface was estimated. The aspect ratio surface was then applied in land use regression models for estimating nitrogen dioxide (NO2) and nitrogen oxide (NO) concentrations in the City of Vancouver, Canada. LUR models, which typically are unable to incorporate urban morphometric characteristics, improved significantly when aspect ratio was included. The prediction power (variance explained) increased from 56% to 67% for NO2, and from 72% to 85% for NO. The standard error (SE) and root mean square error (RMSE) of prediction decreased 10% and 11% for NO2, and 25% and 26% for NO when aspect ratio was added to LUR models. In addition to improving the estimation of spatial variability of urban air pollution concentrations, the methods developed here to produce building height surfaces may also improve understanding of urban noise pollution, in estimating sky view factors and in classifying flow regions for urban climate applications.