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Comparing the performance of air pollution models for nitrogen dioxide and ozone in the context of a multi-level epidemiological analysis.

Research output: Contribution to journalArticlepeer-review

Klea Katsouyanni, Barbara Butland, Evangelia Samoli, Richard Atkinson, Benjamin Barratt, Sean Beevers, Nutthida Kitwiroon, Konstantina Dimakopoulou, Sophia Rodopoulou, Joel Schwartz

Original languageEnglish
Number of pages22
JournalEnvironmental Epidemiology
Accepted/In press26 Mar 2020


King's Authors


Background: Using modelled air pollutant predictions as exposure variables in
epidemiological analyses can produce bias in health effect estimation. We used statistical simulation to estimate these biases and compare different air pollution models for London.
Methods: Our simulations were based on a sample of 1,000 small geographical areas within London, UK. “True” pollutant data (daily mean nitrogen dioxide (NO2) and ozone (O3)) were simulated to include spatio-temporal variation and spatial covariance. All-cause mortality and cardiovascular hospital admissions were simulated from “true” pollution data using prespecified effect parameters for short and long-term exposure within a multi-level Poisson model. We compared: Land Use Regression (LUR) models, dispersion models, LUR models
including dispersion output as a spline (hybrid1) and generalised additive models combining splines in LUR and dispersion outputs (hybrid2). Validation datasets (model versus fixed-site monitor) were used to define simulation scenarios.
Results: For the LUR models, bias estimates ranged from -56% to +7% for short-term exposure and -98% to -68% for long-term exposure and for the dispersion models from -33% to -15% and -52% to +0.5% respectively. Hybrid1 provided little if any additional benefit but hybrid2 appeared optimal in terms of bias estimates for short-term (-17% to +11%) and long-term (-28% to +11%) exposure and in preserving coverage probability and statistical power.
Conclusion: Although exposure error can produce substantial negative bias (i.e. towards the null), combining outputs from different air pollution modelling approaches may reduce bias in health effect estimation leading to improved impact evaluation of abatement policies.

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