In this study, the effects on daily mortality in Stockholm associated with short-term exposure to ultrafine particles (measured as number of particles with a diameter larger than 4 nm, PNC4), black carbon (BC) and coarse particles (PM2.5–10) have been compared with the effects from more common traffic-pollution indicators (PM10, PM2.5 and NO2) and O3 during the period 2000–2016. Air pollution exposure was estimated from measurements at a 20 m high building in central Stockholm. The associations between daily mortality lagged up to two days (lag 02) and the different air pollutants were modelled by using Poisson regression. The pollutants with the strongest indications of an independent effect on daily mortality were O3, PM2.5–10 and PM10. In the single-pollutant model, an interquartile range (IQR) increase in O3 was associated with an increase in daily mortality of 2.0% (95% CI: 1.1–3.0) for lag 01 and 1.9% (95% CI: 1.0–2.9) for lag 02. An IQR increase in PM2.5–10 was associated with an increase in daily mortality of 0.8% (95% CI: 0.1–1.5) for lag 01 and 1.1% (95% CI: 0.4–1.8) for lag 02. PM10 was associated with a significant increase only at lag 02, with 0.8% (95% CI: 0.08–1.4) increase in daily mortality associated with an IQR increase in the concentration. NO2 exhibits negative associations with mortality. The significant excess risk associated with O3 remained significant in two-pollutant models after adjustments for PM2.5–10, BC and NO2. The significant excess risk associated with PM2.5–10 remained significant in a two-pollutant model after adjustment for NO2. The significantly negative associations for NO2 remained significant in two-pollutant models after adjustments for PM2.5–10, O3 and BC. A potential reason for these findings, where statistically significant excess risks were found for O3, PM2.5–10 and PM10, but not for NO2, PM2.5, PNC4 and BC, is behavioral factors that lead to misclassification in the exposure. The concentrations of O3 and PM2.5–10 are in general highest during sunny and dry days during the spring, when exposure to outdoor air tend to increase, while the opposite applies to NO2, PNC4 and BC, with the highest concentrations during the short winter days with cold weather, when people are less exposed to outdoor air.
Abstract. Air pollution concentrations have been decreasing in many cities in the developed countries. We have estimated time trends and health effects associated with exposure to NOx, NO2, O3, and PM10 (particulate matter) in the Swedish cities Stockholm, Gothenburg, and Malmö from the 1990s to 2015. Trend analyses of concentrations have been performed by using the Mann–Kendall test and the Theil–Sen method. Measured concentrations are from central monitoring stations representing urban background levels, and they are assumed to indicate changes in long-term exposure to the population. However, corrections for population exposure have been performed for NOx, O3, and PM10 in Stockholm, and for NOx in Gothenburg. For NOx and PM10, the concentrations at the central monitoring stations are shown to overestimate exposure when compared to dispersion model calculations of spatially resolved, population-weighted exposure concentrations, while the reverse applies to O3. The trends are very different for the pollutants that are studied; NOx and NO2 have been decreasing in all cities, O3 exhibits an increasing trend in all cities, and for PM10, there is a slowly decreasing trend in Stockholm, a slowly increasing trend in Gothenburg, and no significant trend in Malmö. Trends associated with NOxand NO2 are mainly attributed to local emission reductions from traffic. Long-range transport and local emissions from road traffic (non-exhaust PM emissions) and residential wood combustion are the main sources of PM10. For O3, the trends are affected by long-range transport, and there is a net removal of O3 in the cities. The increasing trends are attributed to decreased net removal, as NOx emissions have been reduced. Health effects in terms of changes in life expectancy are calculated based on the trends in exposure to NOx, NO2, O3, and PM10 and the relative risks associated with exposure to these pollutants. The decreased levels of NOx are estimated to increase the life expectancy by up to 11 months for Stockholm and 12 months for Gothenburg. This corresponds to up to one-fifth of the total increase in life expectancy (54–70 months) in the cities during the period of 1990–2015. Since the increased concentrations in O3 have a relatively small impact on the changes in life expectancy, the overall net effect is increased life expectancies in the cities that have been studied.
In this study, an Air Quality Health Index (AQHI) for Stockholm is introduced as a tool to capture the combined effects associated with multi-pollutant exposure. Public information regarding the expected health risks associated with current or forecasted concentrations of pollutants and pollen can be very useful for sensitive persons when planning their outdoor activities. For interventions, it can also be important to know the contribution from pollen and the specific air pollutants, judged to cause the risk. The AQHI is based on an epidemiological analysis of asthma emergency department visits (AEDV) and urban background concentrations of NOx, O3, PM10 and birch pollen in Stockholm during 2001–2005. This analysis showed per 10 µg·m–3 increase in the mean of same day and yesterday an increase in AEDV of 0.5% (95% CI: −1.2–2.2), 0.3% (95% CI: −1.4–2.0) and 2.5% (95% CI: 0.3–4.8) for NOx, O3 and PM10, respectively. For birch pollen, the AEDV increased with 0.26% (95% CI: 0.18–0.34) for 10 pollen grains·m–3. In comparison with the coefficients in a meta-analysis, the mean values of the coefficients obtained in Stockholm are smaller. The mean value of the risk increase associated with PM10 is somewhat smaller than the mean value of the meta-coefficient, while for O3, it is less than one fifth of the meta-coefficient. We have not found any meta-coefficient using NOx as an indicator of AEDV, but compared to the mean value associated with NO2, our value of NOx is less than half as large. The AQHI is expressed as the predicted percentage increase in AEDV without any threshold level. When comparing the relative contribution of each pollutant to the total AQHI, based on monthly averages concentrations during the period 2015–2017, there is a tangible pattern. The AQHI increase associated with NOx exhibits a relatively even distribution throughout the year, but with a clear decrease during the summer months due to less traffic. O3 contributes to an increase in AQHI during the spring. For PM10, there is a significant increase during early spring associated with increased suspension of road dust. For birch pollen, there is a remarkable peak during the late spring and early summer during the flowering period. Based on monthly averages, the total AQHI during 2015–2017 varies between 4 and 9%, but with a peak value of almost 16% during the birch pollen season in the spring 2016. Based on daily mean values, the most important risk contribution during the study period is from PM10 with 3.1%, followed by O3 with 2.0%.
Combustion-related carbonaceous particles seem to be a better indicator of adverse health effects compared to PM2.5 and PM10. Historical studies are based on black smoke (BS), but more recent studies use absorbance (Abs), black carbon (BC) or elemental carbon (EC) as exposure indicators. To estimate health risks based on BS, we review the literature regarding the relationship between Abs, BS, BC and EC. We also discuss the uncertainties associated with the comparison of relative risks (RRs) based on these conversions. EC is reported to represent a proportion between 5.2% and 27% of BS with a mean value of 12%. Correlations of different metrics at one particular site are higher than when different sites are compared. Comparing all traffic, urban and rural sites, there is no systematic site dependence, indicating that other properties of the particles or errors affect the measurements and obscure the results. It is shown that the estimated daily mortality associated with short-term levels of EC is in the same range as PM10, but this is highly dependent on the EC to BS relationship that is used. RRs for all-cause mortality associated with short-term exposure to PM10 seem to be higher at sites with higher EC concentrations, but more data are needed to verify this.
Urban air pollutant emissions and concentrations vary throughout the year due to various factors, e.g., meteorological conditions and human activities. In this study, seasonal variations in daily mortality associated with increases in the concentrations of PM10 (particulate matter), PM2.5–10 (coarse particles), BC (black carbon), NO2 (nitrogen dioxide), and O3 (ozone) were calculated for Stockholm during the period from 2000 to 2016. The excess risks in daily mortality are presented in single and multi-pollutant models during the whole year and divided into four different seasons, i.e., winter (December–February), spring (March–May), summer (June–August), and autumn (September–November). The excess risks in the single-pollutant models associated with an interquartile range (IQR) increase for a lag 02 during the whole year were 0.8% (95% CI: 0.1–1.4) for PM10, 1.1% (95% CI: 0.4–1.8) for PM2.5–10, 0.5% (95% CI: −0.5–1.5) for BC, −1.5% (95% CI: −0.5–−2.5) for NO2, and 1.9% (95% CI: 1.0–2.9) for O3. When divided into different seasons, the excess risks for PM10 and PM2.5–10 showed a clear pattern, with the strongest associations during spring and autumn, but with weaker associations during summer and winter, indicating increased risks associated with road dust particles during these seasons. For BC, which represents combustion-generated particles, the pattern was not very clear, but the strongest positive excess risks were found during autumn. The excess risks for NO2 were negative during all seasons, and in several cases even statistically significantly negative, indicating that NO2 in itself was not harmful at the concentrations prevailing during the measurement period (mean values < 20 µg m−3). For O3, the excess risks were statistically significantly positive during “all year” in both the single and the multi-pollutant models. The excess risks for O3 in the single-pollutant models were also statistically significantly positive during all seasons.
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