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Background/objectives: Meteorological factors seem to influence stroke incidence, however, the complex association between weather and stroke remains unclear. Possible explanations from the literature do not categorize into subdivisions of ischemic strokes, only have small patient numbers, or refer to a selection of isolated weather elements without investigating weather changes and more., Method: In this exploratory trial, almost 18,000 stroke cases from a single stroke center in Southern Germany were analyzed from 2006 to 2015 and classified into the main subgroups of strokes and subdivisions of ischemic stroke etiologies applying the Trial of Org 10172 in Acute Stroke Treatment classification. For each stroke event, the air mass classification was determined from a subset of 7 air mass categories. Relative excess morbidities were derived for the 7 different air mass categories, taking into account the day of the event and up to 2 and 5 days preceding the stroke event., Results: Statistically significant findings (α ≤0.1) reveal that dry tropical air masses were associated with a lower/higher risk for hemorrhagic (HEM)/macroangiopathic strokes (MAS), respectively. Dry polar air masses were associated with a higher risk for intracerebral bleedings and lower risk for ischemic stroke subtypes. Moist air masses were associated with a reduced incidence of MAS. A strong temperature increase 5 days prior to the event was associated with a lower risk of HEM strokes. Temperature increases were associated with lower risks for MAS and cardio-embolic strokes. Significant temperature decreases were associated with a higher risk of MAS., Conclusions: Temperature effects were dependent on both air masses and temperature changes within 5 days prior to the event and were associated with statistically relevant changes in stroke incidence. Decisive factors such as etiology, age, sex, and risk factors were also taken into account., (© 2019 S. Karger AG, Basel.)

Background and Aims: Epidemiological studies have shown adverse effects of ambient air pollutants on health with inflammation and oxidative stress playing an important role. We examine the association between blood biomarkers of inflammation and coagulation and physical attributes of particulate matter which are not routinely measured such as particle length or surface area concentration and apparent density of PM., Methods: Between 3/2007 and 12/2008 187 non-smoking individuals with type 2 diabetes mellitus (T2D) or impaired glucose tolerance (IGT) were examined within the framework of the KORA Study in Augsburg, Germany. In addition, we selected 87 participants with a potential genetic predisposition on detoxifying and inflammatory pathways. This was defined by the null polymorphism for glutathione S-transferase M1 in combination with a certain single nucleotide polymorphism on the C-reactive protein (CRP) gene (rs1205) or the fibrinogen gene (rs1800790). Participants had blood drawn up to seven different times, resulting in 1765 blood samples. Air pollutants were collected at a central measurement station and individual 24-h averages calculated. Associations between air pollutants and high sensitivity CRP, myeloperoxidase (MPO), interleukin (IL)-6 and fibrinogen were analysed using additive mixed models., Results: For the panel with genetic susceptibility, increases were seen for CRP and MPO with most attributes, specifically particle length and active surface concentration. The %change of geometric mean and 95% confidence intervals for the 5-day average exposure for CRP and MPO were 34.6% [21.8;48.8] and 8.3% [3.2;13.6] per interquartile range increase of particle length concentration and 29.8% [15.9;45.3] and 10.4 [4.4;16.7] for active surface area. Results for the panel of T2D and IGT and the other blood biomarkers were less conclusive., Conclusions: Particle length concentration and active surface concentration showed strong positive associations with blood biomarkers reflecting inflammation. These air pollution metrics might reflect harmful aerosol properties better than particulate mass or number concentration. They might therefore be important for epidemiological studies., (Copyright © 2016 Elsevier Inc. All rights reserved.)

The pathophysiological pathways linking particulate air pollution to cardiovascular disease are still not fully understood. We examined the association between ambient air pollutants and blood markers of inflammation and coagulation/fibrinolysis in three potentially susceptible populations. Three panels of non-smoking individuals were examined between 3/2007 and 12/2008: 1) with type 2 diabetes mellitus (T2D, n=83), 2) with impaired glucose tolerance (IGT, n=104), and 3) with a potential genetic predisposition which could affect detoxifying and inflammatory pathways (n=87) defined by the null polymorphism for glutathione S-transferase M1 (GSTM1) in combination with a certain single nucleotide polymorphism on the C-reactive protein (CRP) or the fibrinogen gene. Study participants had blood drawn up to seven times every four to six weeks. In total, 1765 blood samples were analysed for CRP, interleukin (IL)-6, soluble CD40 ligand (sCD40L), fibrinogen, myeloperoxidase (MPO), and plasminogen activator inhibitor-1 (PAI-1). Hourly mean values of particulate air pollutants, particle number concentrations in different size ranges and gaseous pollutants were collected at fixed monitoring sites and individual 24hour averages calculated. Associations between air pollutants and blood markers were analysed for each panel separately and taking the T2D panel and the IGT panel together, using additive mixed models adjusted for long-term time trend and meteorology. For the panel with potential genetic susceptibility, CRP and MPO increased for most lags, especially with the 5-day average exposure (% change of geometric mean and 95% confidence interval: 22.9% [12.0;34.7] for CRP and 5.0% [0.3;9.9] for MPO per interquartile range of PM2.5). Small positive associations were seen for fibrinogen while sCD40L, PAI-1 and IL-6 mostly decreased in association with air pollution concentrations. Except for positive associations for fibrinogen we did not see significant results with the two other panels. Participants with potential genetic susceptibility showed a clear association between inflammatory blood biomarkers and ambient air pollutants. Our results support the hypothesis that air pollution increases systemic inflammation especially in susceptible populations which may aggravate atherosclerotic diseases and induce multi-organ damage., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

Unlabelled: In many European cities mass concentrations of PM10 (particles less than 10 microm in size) are still exceeding air quality standards as set by the European Commission in 1999. As a consequence, many cities introduced low emission zones (LEZs) to improve air quality and to meet the limit values. In Germany currently 48 LEZs are in operation. By means of dispersion modeling, PM10 concentrations were estimated to decrease up to 10%. Analysis of PM10 levels conducted for Cologne, Berlin, and Munich some time after the LEZs were introduced showed reduction of PM10 mass concentration in the estimated range. The PM10 particle fraction is, however composed of particles with varying toxicity, of which diesel soot is highly health relevant. An evaluation of air quality data conducted in Berlin showed that in 2010 traffic-related soot concentrations measured along major roads decreased by 52% compared to 2007. Diesel particle emissions in Berlin were reduced in 2012 by 63% compared to a business-as-usual scenario (reference year 2007). A strong reduction of the traffic-related particle fraction of PM2.5 was also reported for Munich. Therefore, it is likely that the effects of LEZs are considerably more significant to human health than was anticipated when only considering the reduction of PM10 mass concentrations., Implications: The implementation of low emission zones in German cities might result in a reduction of PM10 levels concentrations by up to 10%. However, it is difficult to show a reduction of PM10 annual averages in this order of magnitude as meteorology has a large impact on the year-to-year variation of PM mass concentrations. Monitoring of other PM metrics such as black smoke (BS) or elemental carbon (EC) might be a better strategy for evaluating LEZs effects. The benefit of low emission zones on human health is far greater than is presently visible from routine measurements of PM10.

The success of epidemiological studies depends on the use of appropriate exposure variables. The purpose of this study is to extract a relatively small selection of variables characterizing ambient particulate matter from a large measurement data set. The original data set comprised a total of 96 particulate matter variables that have been continuously measured since 2004 at an urban background aerosol monitoring site in the city of Augsburg, Germany. Many of the original variables were derived from measured particle size distribution (PSD) across the particle diameter range 3 nm to 10 μm, including size-segregated particle number concentration, particle length concentration, particle surface concentration and particle mass concentration. The data set was complemented by integral aerosol variables. These variables were measured by independent instruments, including black carbon, sulfate, particle active surface concentration and particle length concentration. It is obvious that such a large number of measured variables cannot be used in health effect analyses simultaneously. The aim of this study is a pre-screening and a selection of the key variables that will be used as input in forthcoming epidemiological studies. In this study, we present two methods of parameter selection and apply them to data from a two-year period from 2007 to 2008. We used the agglomerative hierarchical cluster method to find groups of similar variables. In total, we selected 15 key variables from 9 clusters which are recommended for epidemiological analyses. We also applied a two-dimensional visualization technique called "heatmap" analysis to the Spearman correlation matrix. 12 key variables were selected using this method. Moreover, the positive matrix factorization (PMF) method was applied to the PSD data to characterize the possible particle sources. Correlations between the variables and PMF factors were used to interpret the meaning of the cluster and the heatmap analyses., (Copyright © 2012 Elsevier B.V. All rights reserved.)