Personal exposure levels to O3, NOx and PM10 and the association to ambient levels in two Swedish cities

Abstract

Exposure to air pollution is of great concern for public health although studies on the associations between exposure estimates and personal exposure are limited and somewhat inconsistent. The aim of this study was to quantify the associations between personal nitrogen oxides (NOx), ozone (O3) and particulate matter (PM10) exposure levels and ambient levels, and the impact of climate and time spent outdoors in two cities in Sweden. Subjects (n = 65) from two Swedish cities participated in the study. The study protocol included personal exposure measurements at three occasions, or waves. Personal exposure measurements were performed for NOx and O3 for 24 h and PM10 for 24 h, and the participants kept an activity diary. Stationary monitoring stations provided hourly data of NOx, O3 and PM, as well as data on air temperature and relative humidity. Data were analysed using mixed linear models with the subject-id as a random effect and stationary exposure and covariates as fixed effects. Personal exposure levels of NOx, O3 and PM10 were significantly associated with levels measured at air pollution monitoring stations. The associations persisted after adjusting for temperature, relative humidity, city and wave, but the modelled estimates were slightly attenuated from 2.4% (95% CI 1.8–2.9) to 2.0% (0.97–2.94%) for NOx, from 3.7% (95% CI 3.1–4.4) to 2.1% (95% CI 1.1–2.9%) for O3 and from 2.6% (95% 0.9–4.2%) to 1.3% (95% CI − 1.5–4.0) for PM10. After adding covariates, the degree of explanation offered by the model (coefficient of determination, or R2) did not change for NOx (0.64 to 0.63) but increased from 0.46 to 0.63 for O3, and from 0.38 to 0.43 for PM10. Personal exposure to NOx, O3 and PM has moderate to good association with levels measured at urban background sites. The results indicate that stationary measurements are valid as measure of exposure in environmental health risk assessments, especially if they can be refined using activity diaries and meteorological data. Approximately 50–70% of the variation of the personal exposure was explained by the stationary measurement, implying occurrence of misclassification in studies using more crude exposure metrics, potentially leading to underestimates of the effects of exposure to ambient air pollution.

Birch pollen, air pollution and their interactive effects on airway symptoms and peak expiratory flow in allergic asthma during pollen season – a panel study in Northern and Southern Sweden

“…Furthermore, studies have shown central monitoring stations to be a reasonable proxy for personal exposure to birch pollen [ 42 ]. Also, monitoring station data correlated well with personal exposure in the study [ 27 ]. The panel data collected both in, and outside, pollen season ensured a substantial exposure contrast.…”

“…Birch pollen counts were obtained from centrally located pollen monitoring stations using standard methods [ 26 ]. Ambient concentrations of the air pollutants NO x , O 3 and PM 2.5 , and meteorological data of relative humidity and temperature were measured at centrally located fixed monitoring stations in the two study centers and obtained from the environmental authorities as previously described [ 27 ]. In this study, we used PM 2.5 as this was available in both locations for the study period.…”

“…The data were converted to 24-hour means and were assigned to each study date and lagged values were calculated [ 27 ]. The birch pollen season was defined as the first occurrence of 5 consecutive days where 4 days had pollen counts > 0 [ 26 ] (not dissimilar to the recently proposed pollen exposure threshold from recent position paper) [ 28 ].…”

Linkages between particulate and gaseous pollutants over two comparable environments

“…Formation of secondary PM 10 particles is unlikely (because it requires ambient environmental conditions favourable to GPC conditions), so there might been some common sources of PM and NO X (like vehicles). Multiple studies have showed similar ndings (Haga et al, 2021;Rodriguez et al, 2019). Nitrogen Family (NO X , NO, NO 2 ) showed signi cant positive correlation among three, with highest correlation between NO and NO X (0.87), followed by NO 2 and NO X (0.83), and NO and NO 2 (0.58).…”

Validation study of WindTrax reverse dispersion model coupled with a sensitivity analysis of model-specific settings

“…In last years, urbanization and industrialization have been major contributing factors to poor air quality. As the air quality deteriorates, exposure to air pollution remains a fundamental concern to public health: chemical species in the atmosphere, such as NO2, SO2, CO, PM10, PM2.5, C6H6, could severely damage the health of the population (Breton et al, 2021;Haga et al, 2021).…”