In this panel study 65 participating individuals from two Swedish cities with substantially different background pollution levels and meteorology, reported their daily activities, and personal exposure was simultaneously monitored for up to three measurement periods. We found that stationary measures of exposure to NOx, O3 and PM10 were statistically significantly associated with personal exposure in unadjusted, mixed models with individual as random effects (Table 4). After adding covariates, such as meteorological variables, city and wave, stationary PM was no longer statistically significantly associated with personal PM, but for all three outcomes the model fits were improved after adding covariates as indicated by increases in R2. The fully adjusted models of NOX and O3 explained more than 50% of the variation in the personal exposure, although the number of observations decreased due to dropout and non-participation, especially for the self-reported exposure in the activity diary. In comparison, the crude correlation between stationary and personal monitoring tended to be weak, with r values ranging between 0.12 and 0.46 (Table S1), with personal measured being lower (Table 2) indicating that using the mixed-model design to account for personal behaviour and characteristics has advantages over correlation methods.
Participants in Gothenburg generally reported spending more time outdoors in dense traffic which is logical as Gothenburg is a larger city with substantially more dense traffic and volume compared to Umeå (Carlsen et al. 2017) (Table 3). In general, people spend most of their time indoors. In the current study, the participants reported spending an average of around 21 hours indoors in both spring (wave 1 and 3) and winter (wave 2).
The time outdoors in dense traffic was significantly associated with personal NOX exposure and as expected, contributed significantly to the individuals’ exposure (table 5). For ozone, total time spent outdoors, time spent outdoors not in traffic, and time inside were significantly associated with personal exposure to ozone (time spent indoors was negatively correlated), whereas the association with time spent in dense traffic was also positive, it did not reach statistical significance, possibly because of the complex chemical reactivity pattern of ozone in dense traffic.
Time spent indoors was negatively correlated with all personal exposures, although it only reached statistical significance for O3. Also, for PM10, the association between personal exposure and stationary measurements were stronger after adjusting for time spent in dense traffic, although the association for time spent in traffic did not reach statistical significance, perhaps because time spend in dense traffic strongly influence the personal exposure measurements (Table 5).
To improve the adjustment for location, we adjusted for modelled annual background levels of PM2,5 instead of city. However, this variable did not improve the model fit and did not modify the effect of the stationary PM10 exposure (Table S3) in the short term. As Gothenburg is in the southern part of Sweden, a larger proportion of air pollutants is due to long range transport from more southern parts of Europe compared to Umeå in the northern part of Sweden. However, air pollutants are generated both locally and transported some distances with the wind but have little within-city gradient and are thus not likely to influence the results of this study. Furthermore, because of its reactivity, NOx decays in the atmosphere within days before it can be subjected to long-range transport away from the source. The size of the proportion of PM contributed from long-range transport is a matter of debate and wide ranges have been reported. Johannesson et al. 2007 observed associations between 24-hours of urban background and personal levels of PM2.5 particles with a correlation coefficient of 0.61 (Spearman) but spending time outdoors was only a predictor for the Fe-trace element. In a multi-centre study in heterogenous environments the authors compared land use regression (LUR)-based exposure with personal exposure and found that LUR predicted personal exposure to soot and NO2, in some sites with R2 from 0.35–0.44 (Montagne et al. 2013) For PM2.5 and NOX, there were no significant correlations. Measuring in elderly subjects during spring, summer and winter, found that LUR model-predicted ozone and PM2.5 showed moderately associations with personal exposure levels, whereas model-predicted NO2 was not associated with personal NO2 (Sahsuvaroglu et al. 2009). Thus, there are no consensus regarding personal exposure to air pollutants based on stationary measurements and therefore until now, it has also been difficult to sort out if certain exposures are more harmful, which to some extent can be explained by rough exposure assessments that will blur the effects of specific exposures. In studies that aimed to quantify the effect of measurement error, it was found that risk estimates increased after adjustment for measurement error (Hart et al. 2015). This important point will be addressed in future analysis of the collected data as no health risks were addressed in the current study
Strengths and limitations
The study design with thorough sampling and repeated measures on the same individual during three monitoring waves as well as parallel self-reported activity ensures that our data has high internal validity. The study was performed using an identical study protocol and identical equipment for measurements of personal exposure, in two distinct geographical locations with different meteorology and background exposure, which ensured that the data had good variability.
Due to various reasons, among them a comprehensive study protocol and a few lost samplers, not all subjects were included in all measurements in all study waves, however, a comparison of the demographic characteristics and exposure of the individuals who did not complete all exposure measurements (n = 18) versus those who did (n = 47) found no statistically significant differences.