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.
Highlights
Ambient air pollution is the largest environmental public health risk and is estimated to be responsible for approximately one in every ninth premature deaths annually worldwide (WHO, 2016)
The aim of this study was to quantify the agreement between urban background and personal exposure of NOx, O3 and PM10 to increase our knowledge of monitored concentrations at urban background stations as substitutes for personal exposure in population studies
The personal NOx exposure levels in Umeå as well as the personal PM10 exposure levels in Gothenburg was an exception, as the levels were similar or higher than levels registered at the stationary monitoring stations (Table 2 and Fig. 2)
Summary
Ambient air pollution is the largest environmental public health risk and is estimated to be responsible for approximately one in every ninth premature deaths annually worldwide (WHO, 2016). As air pollution is a complex mixture of different compounds, having both natural and anthropogenic origin, the ambient concentrations may vary depending on sources and local meteorological factors such as temperature, relative humidity, wind speed and direction (Grundström et al, 2015). In densely populated urban areas, traffic-related air pollutants at street level such as particulate matter (PM), nitrogen oxides (NOx) and ozone (O3) are of greatest concern as they are associated with severe both acute- and long-term health effects, respiratory disease (WHO, 2016). Particulate matter (PM), complex mixtures of solid and liquid particles suspended in the air, can be of both anthropogenic and natural origin and are Environ Monit Assess (2021) 193: 674 characterized by their size. The most prominent sources of PM10 are local emissions related to traffic (Segersson et al, 2017), but PM10 levels are influenced by long-range transport, which may account for up to 70% of the background levels in urban areas (Carlsen et al, 2020; Petit et al, 2019)
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