Can dispersion modeling of air pollution be improved by land-use regression? An example from Stockholm, Sweden

Both dispersion modeling (DM) and land-use regression modeling (LUR) are often used for assessment of long-term air pollution exposure in epidemiological studies, but seldom in combination. We developed a hybrid DM–LUR model using 93 biweekly observations of NO(x) at 31 sites in greater Stockholm (Sweden). The DM was based on spatially resolved topographic, physiographic and emission data, and hourly meteorological data from a diagnostic wind model. Other data were from land use, meteorology and routine monitoring of NO(x). We built a linear regression model for NO(x), using a stepwise forward selection of covariates. The resulting model predicted observed NO(x) (R(2)=0.89) better than the DM without covariates (R(2)=0.68, P-interaction <0.001) and with minimal apparent bias. The model included (in descending order of importance) DM, traffic intensity on the nearest street, population (number of inhabitants) within 100 m radius, global radiation (direct sunlight plus diffuse or scattered light) and urban contribution to NO(x) levels (routine urban NO(x), less routine rural NO(x)). Our results indicate that there is a potential for improving estimates of air pollutant concentrations based on DM, by incorporating further spatial characteristics of the immediate surroundings, possibly accounting for imperfections in the emission data.