Effect of monitoring network design on land use regression models for estimating residential NO2 concentration

- Land-use regression (LUR) models for NO2 were evaluated using a dispersion model.
- The number of monitoring sites improved LUR model performance, but not > ∼30 sites.
- Networks including sites in populated areas better estimated across residential NO2.
- Roadside sites needed to better characterise the high end of residential NO2.
- No specific monitoring site design estimated both overall and high NO2 levels well.

Land-use regression (LUR) models are increasingly used to estimate exposure to air pollution in urban areas. An appropriate monitoring network is an important component in the development of a robust LUR model. In this study concentrations of NO2 were simulated by a dispersion model at 'virtual' monitoring sites in 54 network designs of varying numbers and types of site, using a 25 km2 area in Edinburgh, UK, as an example location. Separate LUR models were developed for each network. The LUR models were then used to estimate NO2 concentration at all residential addresses, which were evaluated against the dispersion-modelled concentration at these addresses. The improvement in predictive capability of the LUR models was insignificant above ∼30 monitoring sites, although more sites tended to yield more precise LUR models. Monitoring networks containing sites located within highly populated areas better estimated NO2 concentrations across all residential locations. LUR models constructed from networks containing more roadside sites better characterised the high end of residential NO2 concentrations but had increased errors when considering the whole range of concentrations. No particular composition of monitoring network resulted in good estimation simultaneously across all residential NO2 concentration and of the highest NO2 levels. This evaluation with dispersion modelling has shown that previous LUR model validation methods may have been optimistic in their assessment of the model's predictive performance at residential locations.