Development of Land Use Regression Models for PM_2.5, PM_2.5 Absorbance, PM₁₀ and PM_coarse in 20 European Study Areas; Results of the ESCAPE Project

TLDR

Land Use Regression models are increasingly employed to capture fine‑scale spatial variation in air pollution and estimate individual exposure in cohort studies. In ESCAPE, annual concentrations of PM₂.₅, PM₂.₅ absorbance, PM₁₀, and PM_coarse were measured at 20 sites per of 20 European areas and modeled with 2–5 GIS‑derived predictors, mainly traffic indicators, to estimate concentrations at participants’ home addresses. The models explained 71 % of PM₂.₅ variance (range 35–94 %), 89 % for PM₂.₅ absorbance (56–97 %) and 68 % for PM_coarse (32–81 %), with lower R² linked to limited variability or predictors, and cross‑validation R² fell 8–11 % lower, underscoring the need for careful site selection and data handling.

Abstract

Land Use Regression (LUR) models have been used increasingly for modeling small-scale spatial variation in air pollution concentrations and estimating individual exposure for participants of cohort studies. Within the ESCAPE project, concentrations of PM(2.5), PM(2.5) absorbance, PM(10), and PM(coarse) were measured in 20 European study areas at 20 sites per area. GIS-derived predictor variables (e.g., traffic intensity, population, and land-use) were evaluated to model spatial variation of annual average concentrations for each study area. The median model explained variance (R(2)) was 71% for PM(2.5) (range across study areas 35-94%). Model R(2) was higher for PM(2.5) absorbance (median 89%, range 56-97%) and lower for PM(coarse) (median 68%, range 32- 81%). Models included between two and five predictor variables, with various traffic indicators as the most common predictors. Lower R(2) was related to small concentration variability or limited availability of predictor variables, especially traffic intensity. Cross validation R(2) results were on average 8-11% lower than model R(2). Careful selection of monitoring sites, examination of influential observations and skewed variable distributions were essential for developing stable LUR models. The final LUR models are used to estimate air pollution concentrations at the home addresses of participants in the health studies involved in ESCAPE.