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In this systematic literature review and meta-analysis, the association between air pollution exposure and depression was analyzed. This meta-analysis is based on 90 studies investigating the associations between both short- and long-term exposure to various air pollutants and depression, including post-partum depression. These studies were reported according to “The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)” guidelines. The studies included in the meta-analysis comprised of several different air pollutants including nitrogen dioxide (NO2), sulphur dioxide (SO2), carbon monoxide (CO), particulate matter (PM2.5 and PM10), ozone (O3), and black carbon (BC), examined through both measured data and modelled estimates. A total of 17 meta-coefficients were calculated, with statistically significant associations in ten of these, indicating a clear link between air pollution exposure and depression. The meta-coefficient for long-term exposure to PM2.5 was based on 25 studies with a risk increase of 1.044 (95%: 1.024–1.064) associated with a 1 µg m−3 increase. The meta-coefficient for long-term exposure to NO2 was based on 22 studies with a risk increase of 1.011 (95%: 1.003–1.018) associated with a 1 µg m−3 increase. However, a high degree of heterogeneity throughout almost all meta-analyses may restrict the generalizability of our findings. Systematic Review Registration – https://inplasy.com/inplasy-2024-8-0025/, INPLASY INPLASY202480025.

Background: High concentrations of coarse particulate matter PM10 from road dust are a major air quality concern in Visby, Sweden. To mitigate these levels, local authorities replaced soft limestone with crushed granite as an anti-slip material starting in the winter of 2023/2024. This is a follow-up study evaluating the impact of this intervention on PM10 concentrations and the associated short-term respiratory health effects.

Methods: Daily counts of healthcare visits for respiratory diseases (ICD-10: J00–J99) and daily mean PM10 concentrations were analyzed using a quasi-Poisson regression model. This study compared the limestone period (2015–2019) with the granite period (2023–2025), stratified by season (winter/spring and summer/autumn) and age group (children 0–17 years and adults >17 years).

Results: The transition to crushed granite reduced peak PM10 concentrations during the spring. For adults, the relative risks for respiratory visits during winter/spring decreased during the granite period when compared to the limestone period (Wald p < 0.05). However, when considering that there were a majority of non-statistically significant differences when comparing the granite and limestone periods, these results should be interpreted with caution. Among children, more pronounced associations were observed during summer, although no significant differences in risk were detected between the limestone and granite periods.

Conclusions: Although the intervention effectively lowered particle mass concentrations, only minor changes were observed in the overall epidemiological pattern. This suggests that public health improvements may be limited by factors beyond total mass reduction, such as particle mineralogy or seasonal exposure dynamics.

Fine particulate matter (PM2.5) is a well-known risk factor for premature mortality; however, the relative effect size varies between different studies. The current study aims to estimate the concentration–response function (CFR) between all-cause mortality and PM2.5 while adjusting for differences in population characteristics and exposure assessment method. Separate functions by exposure assessment method by dispersion, land use regression (LUR), and hybrid models as well as monitoring station measurements and by model resolution will be provided. We used the same keywords as those in the review by Chen and Hoek (2020), which included studies up to October 2018. Our updated search covered the period from July 1, 2018, to May 15, 2023, to include more recent publications. A random-effects meta-regression model was developed using the metafor package in R to account for variations in exposure estimates. Exposure assessment methods were categorized into Monitoring, Land-Use Regression (LUR), Dispersion Modeling, and Hybrid approaches, and spatial resolution was classified as high (≤ 1 km) or low (> 1 km). We evaluated linear, logarithmic, inverse, and inverse square root transformations as potential parametric forms of the CRF and selected the one that provided the best fit according to the Akaike Information Criterion (AIC). Additionally, spline-based modeling was employed to test for deviations from this parametric forms in the CFR. The analysis identified 68 eligible studies, incorporating diverse exposure assessment methodologies and geographic regions. In the fully adjusted model, the relative risk (RR) per 10 µg/m3 higher PM2.5 concentration at a mean exposure of 10 µg/m3 was 1.22 (95% CI: 1.02–1.47) and at a mean exposure of 17.85 µg/m3 1.15, (95% CI: 0.97–1.36). In the subgroup analysis, the best-fitting functional form for the CRF varied by exposure assessment method: linear for monitoring, logarithmic for LUR, and inverse for dispersion and hybrid models. For resolution, logarithmic fit best for low-resolution models, and inverse square root for high-resolution models. This study underscores the role of modeling choices in quantifying PM2.5-related health risks. Our analysis offers an updated increased CRF, including the recent evidence, for use in global health risk assessments of particulate air pollution.

The European Union (EU) Ambient Air Quality Directive (AAQD) and Drinking Water Directive (DWD) are aimed at maintaining and improving air quality and ensuring high standards for potable water across the EU. Besides several other indicators, the AAQD and DWD consist of chemical parameters (substances or substance groups) that are regulated within this framework. All the substances are associated with various health outcomes, and many of them are classified as carcinogenic or probably/possibly carcinogenic with causal links. To quantify the health burden of the chemical substances included in the AAQD and DWD, we need information regarding population exposure, current baseline mortality/morbidity rates in the populations, and exposure-response functions (ERFs) or unit risks (URs) from previous epidemiological studies. During this study, we analyzed the availability of ERFs or URs and discussed their applicability in health impact assessments (HIAs). From the HIA perspective, ERFs─in terms of relative risk (RR), standardized mortality ratio (SMR), odds ratio (OR), or UR data─were available for many of the analyzed substances. However, for some substances such as acrylamide, antimony, boron, chlorate and chlorite, copper, microcystin-LR, and selenium, no risk measures could be identified. The aim of this study is to derive ERFs, which will allow HIAs for a larger number of chemicals when exposure data and baseline mortality/morbidity data are available. Currently, HIAs have largely focused only on main ambient pollutants such as particulate matter (PM10), fine particulate matter (PM2.5), nitrogen oxides (NOX), and ozone (O3). In contrast, health risks related to exposure to chemicals are much more diverse, and the health burdens should be quantified to a much greater extent.

Background:The impact of air pollution and road traffic noise on depression remains inconclusive. This study examined long-term residential exposure to air pollutants and road traffic noise in relation to dispensed antidepressant medications.

Methods:A cohort study was conducted using the Swedish Malmö Diet and Cancer Study, with the outcome defined as a first dispensed antidepressant (ATC: N06A) and/or a first clinical depression diagnosis (ICD-10: F32/F33) during follow-up (2007–2011). Exposure was assessed through modelled annual mean concentrations of particulate matter (PM2.5, PM10), nitrogen oxides (NOx), and road traffic noise (Lden). Cox proportional hazards regression was applied, adjusted for potential confounders including sociodemographic and lifestyle factors.

Results:A total of 18 373 individuals were included, among whom 2224 with the outcome (89 % had dispensed antidepressant, 6 % both clinical diagnosis and antidepressant, and 5 % clinical diagnosis) during follow-up. Increased hazard ratios (HRs) were observed in association with long-term air pollution exposure, but only among women. Increased but statistically non-significant HRs were found for road traffic noise in women, while no associations were found in men. For lag1-5 (1–5 years exposure window prior to the event) in women, HRs per 10-unit increase were: PM2.5, 1.29 (95 % CI, 1.09–1.52); PM10, 1.10 (1.03–1.17); NOx, 1.15 (1.04–1.26); and Lden, 1.06 (0.99–1.13). Similar patterns were found for lag1 and in the two-pollutant models.

Conclusion:Long-term exposure to air pollution was positively associated with dispensed antidepressant medications in women but not in men. For road traffic noise, evidence was less clear, but a potential link was seen in women. Though associations were seen only in women, the findings suggest that air quality improvements and possibly also noise reductions may help reduce health burdens related to depressive symptoms.

Objectives: Socioeconomic status (SES) is in many cases related to air pollution exposure, but less is known about its effects on susceptibility to air pollution. The main aim of this study was to analyse the impact of SES on health effects associated with exposure to fine particles (PM_2.5).

Methods: Firstly, a systematic literature review of studies analysing the impacts of SES on health effects related to air pollution exposure was carried out. Secondly, a meta-analysis was performed by analysing studies on long-term mortality associated with exposure to PM_2.5 divided into different SES groups.

Results: The meta-analysis showed that the relative risk (RR) for all-cause mortality associated with PM_2.5 did not depend on individual education or income. It also revealed that adjustment for individual lifestyle factors (such as smoking, alcohol intake, physical activity, eating behaviours, and body mass index), in addition to adjustment for SES, did not significantly change the RR.

Conclusion: The association between all-cause mortality and PM_2.5 did not depend on education or individual income. Due to the high heterogeneity observed, further studies are required to draw firm conclusions.

This systematic literature review investigates the relationship between air pollution exposure and inflammatory bowel disease (IBD), encompassing Crohn's disease (CD) and ulcerative colitis (UC). Despite the growing concern over air pollution's impact on various health outcomes, studies specifically addressing effects on the digestive system, particularly IBD, are relatively rare. This review aims to synthesize the current knowledge on this topic, focusing on the mechanisms underlying these associations and the role of different air pollutants. Following PRISMA guidelines, a systematic literature search of PubMed and Web of Science databases was conducted, yielding 13 epidemiological studies and six mechanistic (toxicological) studies meeting the inclusion criteria. The epidemiological studies examined associations between IBD and various air pollutants, including PM2.5 (particles with an aerodynamic diameter smaller than, or equal to, 2.5 µm), PM10 (particles with an aerodynamic diameter smaller than, or equal to, 10 µm), PM2.5–10 (coarse particles with an aerodynamic diameter in the range of 2.5–10 µm), BC (black carbon), NO2 (nitrogen dioxide), NO (nitrogen monoxide), NOx (nitrogen oxides), N2O (nitrous oxide), CO (carbon monoxide), SO2 (sulfur dioxide), VOC (volatile organic compounds), O3 (ozone), Ox (oxidant capacity), and traffic load. Study methodologies varied among these 13 epidemiological studies, including four cohort studies, two ecological studies, three case-control studies, two studies using two-sample Mendelian randomization, and two longitudinal time-series studies. Eight studies investigated associations with Crohn's disease and ulcerative colitis separately, while five studies analyzed IBD as a whole without distinguishing between CD and UC. Eleven studies found statistically significant associations between air pollution exposure and IBD, although inconsistent results were found in several of these studies. A total number of six mechanistic (toxicological) studies were retrieved. Among these six studies, five were using particulate matter as exposure metric, and one was based on NO2 and O3 as exposure metrics. With a combination of animal, human, and in vitro studies, the results in terms of biological mechanisms indicate that air pollution exposure influences the composition of the gut microbiome, altering metabolic functions within the gut, and creates immunological reactions with inflammation contributing to the development of IBD. Consequently, the results suggest a link between air pollution exposure and both the onset and exacerbation of IBD. However, differences in study design, exposure assessment, and pollutant types make it challenging to draw any firm conclusions. Moreover, the lack of multi-pollutant models in most epidemiological studies makes it difficult to estimate the individual effect of specific air pollutants. This review highlights the need for further research utilizing robust study designs and standardized exposure assessment methods to better understand the impact of air pollution on IBD. By elucidating these associations, policymakers and healthcare professionals can develop effective strategies to mitigate the adverse effects of air pollution on digestive health.

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 (NOx), 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-3increase in PM10the 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-3increase in NOxand 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.

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), PM2.5 (particles with an aerodynamic diameter smaller than or equal to 2.5 µm), BC (black carbon), and NOx (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, PM2.5, BC, and NOx 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, PM2.5, BC, and NOx 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, PM2.5, BC, and NOx 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 NOx, 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.

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), PM2.5–10 (coarse particles), PM2.5 (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 PM2.5–10, and in Tallinn for O3. In the oldest age group (65+), statistically significant effects were shown for both PM2.5–10, PM10, and O3 in Stockholm, and for O3 in Tallinn.