A global spatial-temporal land use regression model for nitrogen dioxide air pollution

Introduction: The World Health Organization (WHO) recently revised its health guidelines for Nitrogen dioxide (NO2) air pollution, reducing the annual mean NO2 level to 10 mu g/m(3) (5.3 ppb) and the 24-h mean to 25 mu g/m(3) (13.3 ppb). NO2 is a pollutant of global concern, but it is also a criteria air pollutant that varies spatiotemporally at fine resolutions due to its relatively short lifetime (similar to hours). Current models have limited ability to capture both temporal and spatial NO2 variation and none are available with global coverage. Land use regression (LUR) models that incorporate timevarying predictors (e.g., meteorology and satellite NO2 measures) and land use characteristics (e.g., road density, emission sources) have significant potential to address this need. Methods: We created a daily Land use regression model with 50 x 50 m(2) spatial resolution using 5.7 million daily air monitor averages collected from 8,250 monitor locations. Results: In cross-validation, the model captured 47%, 59%, and 63% of daily, monthly, and annual global NO2 variation. Daily, monthly, and annual root mean square error were 6.8, 5.0, and 4.4 ppb and absolute bias were 46%, 30%, and 21%, respectively. The final model has 11 variables, including road density and built environments with fine (30 m or less) spatial resolution and meteorological and satellite data with daily temporal resolution. Major roads and satellite-based estimates of NO2 were consistently the strongest predictors of NO2 measurements in all regions. Discussion: Daily model estimates from 2005-2019 are available and can be used for global risk assessments and health studies, particularly in countries without NO2 monitoring.

Automated summary

The World Health Organization (WHO) revised its guidelines for NO2 air pollution, setting stricter limits on the annual and 24-hour mean NO2 levels. Current models are inadequate in capturing the spatiotemporal variations of NO2, but a Land use regression (LUR) model incorporating various predictors has shown potential in addressing this issue.