Application of land use regression to estimate long-term concentrations of traffic-related nitrogen oxides and fine particulate matter

Abstract

Land use regression (LUR) is a promising technique for predicting ambient air pollutant concentrations at high spatial resolution. We expand on previous work by modeling oxides of nitrogen and fine particulate matter in Vancouver, Canada, using two measures of traffic. Systematic review of historical data identified optimal sampling periods for NO and N02. Integrated 14-day mean concentrations were measured with passive samplers at 116 sites in the spring and fall of 2003. Study estimates for annual mean NO and NO2 ranged from 5.4-98.7 and 4.8-28.0 ppb, respectively. Regulatory measurements ranged from 4.8-29.7 and 9.0-24.1 ppb and exhibited less spatial variability. Measurements of particle mass concentration (PM2.5) and light absorbance (ABS) were made at a subset of 25 sites during another campaign. Fifty-five variables describing each sampling site were generated in a Geographic Information System (GIS) and linear regression models for NO, NO2, PM2.5, and ABS were built with the most predictive covariates. Adjusted R(2) values ranged from 0.39 to 0.62 and were similar across traffic metrics. Resulting maps show the distribution of NO to be more heterogeneous than that of NO2, supporting the usefulness of this approach for assessing spatial patterns of traffic-related pollution.