Comparison of land use regression models for NO₂ based on routine and campaign monitoring data from an urban area of Japan

Background: Typically, land use regression (LUR) models have been developed using campaign monitoring data rather than routine monitoring data. However, the latter have advantages such as low cost and long-term coverage. Based on the idea that LUR models representing regional differences in air pollution and regional road structures are optimal, the objective of this study was to evaluate the validity of LUR models for nitrogen dioxide (NO₂) based on routine and campaign monitoring data obtained from an urban area. Method: We selected the city of Suita in Osaka (Japan). We built a model based on routine monitoring data obtained from all sites (routine-LUR-All), and a model based on campaign monitoring data (campaign-LUR) within the city. Models based on routine monitoring data obtained from background sites (routine-LUR-BS) and based on data obtained from roadside sites (routine-LUR-RS) were also built. The routine LUR models were based on monitoring networks across two prefectures (i.e., Osaka and Hyogo prefectures). We calculated the predictability of the each model. We then compared the predicted NO₂ concentrations from each model with measured annual average NO₂ concentrations from evaluation sites. Results: The routine-LUR-All and routine-LUR-BS models both predicted NO₂ concentrations well: adjusted R² = 0.68 and 0.76, respectively, and root mean square error = 3.4 and 2.1 ppb, respectively. The predictions from the routine-LUR-All model were highly correlated with the measured NO₂ concentrations at evaluation sites. Although the predicted NO₂ concentrations from each model were correlated, the LUR models based on routine networks, and particularly those based on all monitoring sites, provided better visual representations of the local road conditions in the city. Conclusion: The present study demonstrated that LUR models based on routine data could estimate local traffic-related air pollution in an urban area. The importance and usefulness of data from routine monitoring networks should be acknowledged.