The old Swedish city Visby, located on the island Gotland, has, for several years, reported higher PM₁₀ concentrations than any other city in Sweden. In Visby, local limestone is used, both in road paving and as sand used for anti-slip measures, resulting in a clear annual pattern of PM₁₀ with the highest concentrations during winter/spring when studded tires are allowed. This study analyzes the short-term associations between PM₁₀ and daily number of patients with acute respiratory problems (ICD–10 diagnoses: J00–J99) seeking care at the hospital or primary healthcare units in Visby during the period of 2013–2019. The daily mean of PM₁₀ was on average 45 µg m⁻³ during winter/spring and 18 µg m⁻³ during summer/autumn. Four outcome categories were analyzed using quasi-Poisson regression models, stratifying for period and adjusting for calendar variables and weather. An increase in respiratory visits was associated with increasing concentrations in PM₁₀ during the summer/autumn period, most prominent among children, where asthma visits increased by 5% (95% CI: 2–9%) per 10 µg m⁻³ increase in PM₁₀. For the winter/spring period, no significant effects were observed, except for the diagnose group ‘upper airways’ in adults, where respiratory visits increased by 1% (95% CI: 0.1–1.9%) per 10 µg m⁻³ increase. According to the results, limestone in particles seem to be relatively harmless at the exposure concentrations observed in Visby, and this is in line with the results from a few experimental and occupational studies.

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), PM_2.5–10 (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 PM_2.5–10, 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 PM_2.5–10 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.

Submicroscopic nanoparticles (NPs) in air have received much attention due to their possible effects on health and wellbeing. Adverse health impacts of air pollution may not only be associated with level of exposure, but also mediated by the perception of the pollution and by beliefs of the exposure being hazardous. The aim of this study was to test a model that describes interrelations between NP pollution, perceived air quality, health risk perception, stress, and sick building syndrome. In the NanoOffice study, the level of NPs was measured and a survey on health risk perception was conducted among 260 employees in twelve office buildings in northern Sweden. Path analyses were performed to test the validity of the model. The data refute the model proposing that the NP exposure level significantly influences stress, chronic diseases, or SBS symptoms. Instead, the perceived exposure influences the perceived risk of NP, and the effect of perceived exposure on SBS and chronic disease is mediated by stress. There was little concern about nanoparticles, despite relatively high levels in some facilities. Perceived pollution and health risk perception may explain a large part of the environmentally induced symptoms and diseases, particularly in relatively low levels of pollution. The research results raise important questions on the physiologically or psychologically mediated health effects of air pollution.

Although the association between air pollution and mortality is well established, less is known about the effects in different age groups. This study analyzes the short-term associations between mortality in different age groups (0–14 years of age, 15–64 years of age, and 65+ years of age) and a number of air pollutants in two relatively clean northern European capitals: Stockholm and Tallinn. The concentrations in PM₁₀ (particles with an aerodynamic diameter smaller than or equal to 10 µm), PM_2.5–10 (coarse particles), PM_2.5 (particles with an aerodynamic diameter smaller than or equal to 2.5 µm), BC (black carbon), PNC₄ (particle number count of particles larger than or equal to 4 nm), NO₂ (nitrogen dioxide), and O₃ (ozone) were measured during the period of 2000–2016 in Stockholm and 2001–2018 in Tallinn (except for BC and PNC₄ which were only measured in Stockholm). The excess risks in daily mortality associated with an interquartile range (IQR) increase in the measured air pollutants were calculated in both single- and multi-pollutant models for lag01 and lag02 (average concentration during the same and the previous day, and the same and the previous two days, respectively) using a quasi-Poisson regression model with a logistic link function. In general, the calculated excess risks per IQR increase were highest in the age group 0–14 years of age in both Stockholm and Tallinn. However, in Stockholm, a statistically significant effect was shown for PM_2.5–10, and in Tallinn for O₃. In the oldest age group (65+), statistically significant effects were shown for both PM_2.5–10, PM₁₀, and O₃ in Stockholm, and for O₃ in Tallinn.

In this study, the long-term mortality effects associated with exposure to PM₁₀ (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), BC (black carbon), and NO_x (nitrogen oxides) were analyzed in a cohort in southern Sweden during the period from 1991 to 2016. Participants (those residing in Malmö, Sweden, born between 1923 and 1950) were randomly recruited from 1991 to 1996. At enrollment, 30,438 participants underwent a health screening, which consisted of questionnaires about lifestyle and diet, a clinical examination, and blood sampling. Mortality data were retrieved from the Swedish National Cause of Death Register. The modeled concentrations of PM₁₀, PM_2.5, BC, and NO_x at the cohort participants’ home addresses were used to assess air pollution exposure. Cox proportional hazard models were used to estimate the associations between long-term exposure to PM₁₀, PM_2.5, BC, and NO_x and the time until death among the participants during the period from 1991 to 2016. The hazard ratios (HRs) associated with an interquartile range (IQR) increase in each air pollutant were calculated based on the exposure lag windows of the same year (lag0), 1–5 years (lag1–5), and 6–10 years (lag6–10). Three models were used with varying adjustments for possible confounders including both single-pollutant estimates and two-pollutant estimates. With adjustments for all covariates, the HRs for PM₁₀, PM_2.5, BC, and NO_x in the single-pollutant models at lag1–5 were 1.06 (95% CI: 1.02–1.11), 1.01 (95% CI: 0.95–1.08), 1.07 (95% CI: 1.04–1.11), and 1.11 (95% CI: 1.07–1.16) per IQR increase, respectively. The HRs, in most cases, decreased with the inclusion of a larger number of covariates in the models. The most robust associations were shown for NO_x, with statistically significant positive HRs in all the models. An overall conclusion is that road traffic-related pollutants had a significant association with mortality in the cohort.

Background: Daily air pollution levels are known to influence the number of patients with acute asthma. We investigated the short-term effects of air pollution exposure on the daily number of asthma medication purchases in the Greater Stockholm area, Sweden. Methods: We conducted a time-series study with data on asthma medication purchases and daily mean values of particulate matter ≤10 µm (PM₁₀), nitrogen oxides (NO _x ), and ozone during 2018–2019. We used nonlinear distributed lag quasi-Poisson regression models to estimate the associations between air pollution levels and medication purchases, adjusting for meteorological variables, pollen levels, day of the week, and long-term trends. The models established linear relationships between air pollutants and the outcome, and potential delayed effects were smoothed with a spline across a lag period of 2 weeks. We applied separate models for each municipality (n = 21) in Greater Stockholm, and calculated pooled estimates to achieve combined results for the whole region. Results: We observed associations between daily levels of air pollution and purchases of asthma medications, most clearly for PM₁₀. The pooled estimates of the relative risks for asthma medication purchases across all 21 municipalities associated with a 10 μg m⁻³ increase in PM₁₀ the same day (lag 0) was 1.7% [95% confidence interval (CI): 1.2%, 2.1%], a cumulative increase of 4.6% (95% CI: 3.7%, 5.6%) over one week (lag 0−6), and a 6.5% (95% CI: 5%, 8%) increase over 2 weeks (lag 0−13). The corresponding pooled effect per 10 μg m⁻³ increase in NO _x and ozone were 2.8% (95% CI: 1.6%, 4.1%) and 0.7% (95% CI: 0%, 1.4%) over 2 weeks (lag 0−13), respectively. Conclusions: Our study revealed short-term associations between air pollution, especially PM₁₀, and purchases of asthma medications.