A Nationwide Land Use Regression Model for Ultrafine Particles

Abstract

TPS 651: Air pollution exposure modeling 1, Exhibition Hall, Ground floor, August 27, 2019, 3:00 PM - 4:30 PM Background: Ultrafine Particles (UFP) are highly variable in space and time and are therefore difficult to predict on a large scale. Previous modelling studies have been published for single metropolitan areas. National models have not been developed which limits exposure for nationwide cohorts. Objectives: The aim of the paper is to create nationwide Land Use Regression (LUR) Model for UFP to be used in epidemiological studies. Method: We used a mobile platform to sample UFP concentrations across the Netherlands for a period of 16 months (2016-2017), with a total of 14.392 road segments sampled 3 times on average. Average concentrations per road segment (~43 seconds of data) and extracted GIS variables were used to develop LUR models using a supervised forward regression approach, as well as a deconvolution, LASSO and random forest algorithm. The deconvolution approach develops separate models for regional, urban and local scales. All models were tested on longer-term UFP measurements in 2014 at 42 sites with an average of 3 times 24 hours of measurements. Results: Out of the four prediction models, the LASSO model predicted the longer-term UFP measurements best (R2 =0.68). The supervised LUR model, deconvoluted approach and random forest algorithm predicted 51%, 62% and 56% of the variation in the longer-term UFP measurements respectively. Conclusions: We were able to develop nationwide models for UFP with good performance. The models will be applied in nationwide epidemiological studies. More modelling sophisticated procedures improved predictive power. The deconvoluted approach improved the standard linear model by distinguishing separate spatial scales.