1. Introduction
While air pollutant emissions occur throughout the whole year, differences in meteorological conditions and human activities cause large seasonal variations in most pollutants. Kukkonen et al. (2000) [
1] showed that meteorological variables in northern Europe vary widely with the seasons and, consequently, those variations are reflected in urban air quality. Apart from anthropogenic and natural emissions, among the main factors influencing seasonal variations in air quality are occurrence of temperature inversions, wind speed, precipitation, and solar radiation [
2]. Particularly in the Nordic countries, where studded winter tires are used, suspension of coarse particles from road surfaces is highly dependent on surface moisture, which shows very large variations during the year, and with a major impact on the seasonal variations in the concentrations of both PM
10 and PM
2.5 [
3].
The seasonal variations in air quality give rise to differences in health effects. In a study from the Netherlands, the associations between daily mortality and short-term variations in a number of air pollutants were analyzed during the period from 1986 to 1994, and the results were, among other things, divided into summer and winter seasons [
4]. They found significantly (95% CI) increased relative risks for all pollutants (PM
10, BS (black smoke), O
3, SO
2, NO
2, CO, SO
4−2, and NO
3−) during both summer and winter, except for O
3 and SO
4−2 during the winter. Moreover, the relative risks for total mortality associated with exposure to these air pollutants were in all cases larger during the summer compared to the winter [
4].
Seasonal variations regarding short-term health effects associated with exposure to PM
10 have been analyzed in a number of studies. When increased mortality associated with an increase in PM
10 was analyzed in 100 cities in the U.S. in the period from 1987 to 2000, a significant association was found for the summer period, but no significant associations were found for the other three seasons. The seasonal pattern was also more pronounced in the northeast region of the U.S., while there were relatively small seasonal variations in the southern regions [
5]. In Flanders, Belgium, where daily mortality associated with PM
10 was calculated during the period from 1997 to 2003, and where temperature and seasons were included as potential effect modifiers, the strongest associations were also found during the summer [
6]. Similarly, a significate effect of PM
10 on mortality increase during the summer was found in Tallinn, Estonia, during the period from 2004 to 2011 [
7]; however, during the winter, a significant decrease in mortality associated with an increase in PM
10 appeared. In a study from China, where the association between daily mortality and an increase in PM
10 was analyzed in 17 Chinese cities during different seasons, significant associations were found for summer and winter, but not for spring and autumn [
8]. In another study from Wuhan, China, conducted from 2001 to 2004, the strongest associations for PM
10 were found during winter [
9]. In a study from Korea, where the associations between PM
10 concentrations and increases in mortality and hospital admissions were analyzed in Seoul from 2000 to 2006, the effects on both mortality and morbidity increased during the summer [
10]. Finally, in a study from Utah, U.S., the association between daily mortality and exposure to PM
10 was examined for the period 1985 to 1992. The largest contribution to excess mortality was for individuals 75+ years old dying in a hospital, and the strongest effect was shown during the spring [
11].
Seasonal variations regarding short-term effects on hospitalization for cardiovascular diseases associated with PM
2.5 were analyzed in New York State during the period 1991 to 2006. The strongest effects, associated with a 10 µg m
−3 increase in PM
2.5, were found during the winter. Temperature modified the PM
2.5 effects on cardiovascular diseases, and these effects were found on low temperature days [
12]. In a study in Tallinn, PM
2.5 increased mortality during the summer, but no effects appeared during the winter [
7].
For particles in the coarse fraction (PM
2.5–10), the difference in short-term effects on daily mortality between two annual periods was analyzed in Stockholm during the period from 2000 to 2008. The associations between PM
2.5–10 and daily mortality were stronger during November to May in comparison with the rest of the year, which can be explained by the high levels of road dust that occurs because of the use of studded tires in Stockholm during this time of the year [
13].
The seasonal variations in short-term effects associated with NO
2 were analyzed in a study from China. The daily mortality associated with NO
2 in the city of Shenzhen in southeastern China during the period from 2013 to 2017 was analyzed during the cold season (November–April) and during the warm season (May–October). Significant excess risks for cardiovascular mortality associated with an increase in NO
2 were found during the cold season at a 2-day lag and at a 6-day lag. However, no significant excess risks were found during the warm season [
14].
The short-term health effects associated with O
3 and their seasonal variations have been studied in U.S., France, and China. When the short-term mortality effects associated with O
3 were analyzed in 20 communities in U.S., and where ten communities represented the northern part and ten represented the southern part, the seasonal variations in the effects estimates exhibited different results in the northern and southern parts. In the southern communities, an increase in O
3 entailed increases in mortality during autumn and winter, while there were negative excess risks during spring and summer. In the northern communities, an increase in mortality was found during spring, summer and autumn, while there was a negative excess risk during the winter. In this study, latitude and seasonal average temperature were identified as effect modifiers [
15]. In a study in nine French urban areas during the period from 1998 to 2006, the association between daily mortality and the daily max-8 h O
3 concentrations was analyzed by season and by temperature strata. The strongest mortality effects were found during the summer and for the highest temperature strata [
16]. In a study from China, the association between O
3 and daily mortality was analyzed in the city of Zhengzhou during the period from 2013 to 2015. Significant excess risks associated with an increase in 10 µg m
−3 24-h average O
3 concentrations at a 1-day lag were found during the cold season, but not during the warm season [
17].
Based on the relatively few studies referenced above, it seems like the strongest excess risks of mortality for PM10 (seven studies) and PM2.5 (only two studies) occur during the summer months. For NO2 and O3, no consistent results in terms of seasonal dependence of excess risks can be seen. However, there are very few studies: only one study for NO2, and only three studies for O3.
The purpose of the current study was to analyze the seasonal variations in the effect estimates of air pollution on mortality in Stockholm. In the earlier analyzes, only annual data were considered, but the seasonal variations were not taken into account [
18]. A special focus was to analyze the causes of the negative excess risks associated with NO
2, and whether the seasons and other pollutants had any effect on these associations. Seasonal differences during the year can potentially affect the results in several ways. People are more likely to stay outdoors and have windows opened during the warm seasons, which can affect the degree of exposure. Annual variations in meteorology and air pollution sources can also be of importance. In Stockholm, the chemical composition of PM
10 and PM
2.5–10 varies throughout the year with a significantly higher proportion of mechanically generated road dust particles during early spring [
3]. Analyzing the associations between mortality and short-term exposure to the above-mentioned air pollutants during different seasons in Stockholm is, therefore, of great interest.
2. Materials and Methods
This study includes residents of Stockholm, with a population that increased from 0.8 to 0.9 million during the period from 2000 to 2016. Population data were obtained from the Swedish Central Bureau of Statistics. Cause of mortality data were obtained from the National Cause of Death Register. Natural cause of mortality is defined on the basis of the underlying cause of death, and these data include the daily number of deaths from non-external causes (ICD-10: A00–R99) occurring among the registered population.
Air pollution exposure was estimated from a central measuring station on the roof-top of a 20 m high building in the central part of Stockholm. The monitoring station was part of the city’s regulatory air pollution control network, and equipped with reference (or equivalent) instruments for regulated pollutants according to the EU air quality directive. These air pollutants included PM
10 (particles with an aerodynamic diameter smaller than or equal to 10 µm), PM
2.5 (particles with an aerodynamic diameter smaller than or equal to 2.5 µm), NO
2 (nitrogen dioxide), and O
3 (ozone) (
Table 1). The O
3 measurements were based on daily maximum 8-h mean values. In addition, the monitoring included measurements of unregulated black carbon (BC), and particles in the coarse fraction (PM
2.5–10) estimated by subtracting PM
2.5 from PM
10. The period from 2000 to 2016 was divided into winter (December–February), spring (March–May), summer (June–August), and autumn (September–November) seasons.
Temperature data were collected from the urban meteorological station Observatorielunden. In this study, daily maximum temperature was used as exposure variable.
The associations between different air pollutants and daily mortality were modelled using a quasi-Poisson regression model with a logistic link function. The concept “quasi-Poisson” refers to a model that adjusts for overdispersed data, and a logistic link function defines the relationship of the dependent variables to the mean of the Poisson distributed independent variables. The modeling procedure was replicated from a previous study [
18] in order to ensure comparability of the results. The model estimated the effect of an interquartile range (IQR) increase in air pollutants on daily mortality for lag 02 (average concentration during the same and the previous two days). The IQR values were calculated based on data for the whole year. Adjustments for other time-varying factors were made by assuming a linear additive effect on a logarithmic scale:
where Yi represents the daily number of deaths from non-external causes, AP
i represents the concentration of a specific or a combination of air pollutants on day i, W
i represents variables controlling for the weather on day i using smooth spline functions for the maximum temperature and snowfall, DOW
i represents the day of the week, and the long-time trend is a smooth function varying over time to capture any long-term and seasonal patterns in mortality. The effects of air pollution were estimated by using a seasonal factor resulting in individual dose-responses for each season while keeping the other variable estimates constant for the whole period. Snowfall was included since it is a risk factor for daily mortality, as described in Auger et al. (2017) [
19]. The smooth function describing the long-term time trend was a penalized regression spline restricted to 5 d.f. (degrees of freedom) per year. All pollutants were modelled by assuming a linear relationship with daily mortality. Air pollutants were first modelled in single-pollutant models, and traffic-related pollutants were included in multi-pollutant models together with O
3 and PM
2.5–10. Temperature effects were adjusted by using two different smooth functions corresponding to the different lag-windows of 0–2 and 3–10. The model allowed for the use of 4 d.f. for each function. All analyses were conducted using R statistical software version 3.6.0 (R Foundation for Statistical Computing, Vienna, Austria).
5. Conclusions
The main objective of this study was to analyze seasonal variations in the excess risks for daily mortality associated with an interquartile range increase in PM10, PM2.5–10, BC, NO2, and O3 in Stockholm during the period from 2000 to 2016. Both single and multi-pollutant models were used in the analysis.
The current study showed a broadly similar pattern for excess risks associated with PM2.5–10 and PM10 with larger risk increases in daily mortality during springtime. The seasonal pattern of the excess risks for PM10 and PM2.5–10 can possibly be explained by the variation in the chemical composition throughout the year, with a larger amount of road dust present during springtime. The excess risks associated with BC were, in most cases, statistically insignificant, which may be due to the fact that the amount of data on which the calculations were based was smaller in comparison with the other air pollutants.
The calculated excess risks for NO2 were negative in almost all cases throughout the whole year. Differences in exposure during the year, which previously was hypothesized as an explanation for the negative excess risks, are unlikely. One possible reason for the negative excess risks associated with NO2 is that the concentrations (on average 14.4 µg m−3) in this study were too low to cause harmful health effects.
The excess risks associated with O3 were most robust in terms of the number of statistically significantly positive relationships, indicating that O3 and its oxidative potential were particularly important in terms of daily mortality associated with air pollution exposure. Higher excess risks were shown during summer and autumn, indicating a higher degree of exposure during the warm seasons.
From a policy point of view, there were clear indications that the health effects associated with exposure to PM2.5–10 and PM10 were most evident during the spring. Additional action strategies to reduce the emissions of road dust particles are, therefore, needed.