Role of highway traffic on spatial and temporal distributions of air pollutants in a Swiss Alpine valley.

Traffic-related air pollutants show high spatial variability near roads, posing a challenge to adequately assess exposures. Recent modeling approaches (e.g. dispersion models, land-use regression (LUR) models) have addressed this but mostly in urban areas where traffic is abundant. In contrast, our study area was located in a rural Swiss Alpine valley crossed by the main North-south transit highway of Switzerland. We conducted an extensive measurement campaign collecting continuous nitrogen dioxide (NO₂), particulate number concentrations (PN), daily respirable particulate matter (PM10), elemental carbon (EC) and organic carbon (OC) at one background, one highway and seven mobile stations from November 2007 to June 2009. Using these measurements, we built a hybrid model to predict daily outdoor NO₂ concentrations at residences of children participating in an asthma panel study. With the exception of OC, daily variations of the pollutants followed the temporal trends of heavy-duty traffic counts on the highway. In contrast, variations of weekly/seasonal means were strongly determined by meteorological conditions, e.g., winter inversion episodes. For pollutants related to primary exhaust emissions (i.e. NO₂, EC and PN) local spatial variation strongly depended on proximity to the highway. Pollutant concentrations decayed to background levels within 150 to 200 m from the highway. Two separate daily NO₂ prediction models were built using LUR approaches with (a) short-term traffic and weather data (model 1) and (b) subsequent addition of daily background NO₂ to previous model (model 2). Models 1 and 2 explained 70% and 91% of the variability in outdoor NO₂ concentrations, respectively. The biweekly averaged predictions from the final model 2 agreed very well with the independent biweekly integrated passive measurements taken at thirteen homes and nine community sites (validation R(2)=0.74). The excellent spatio-temporal performance of our model provides a very promising basis for the health effect assessment of the panel study.