Long-term exposure to ambient air pollution and inflammatory response in the KORA study.

Previous studies consistently showed an association between fine atmospheric particulate matter (PM2.5) and cardiovascular diseases. Concerns about adverse health effects of ultrafine particles (UFP) are growing but long-term studies are still scarce. In this study, we examined the association between long-term exposure to ambient air pollutants and blood biomarkers of inflammation and coagulation, including fibrinogen, high-sensitivity C-reactive protein (hs-CRP), serum amyloid A (SAA) adiponectin and interleukin-6 (IL-6), measured in the German KORA-S4 cohort study (1999-2001). IL-6 was available for older participants only, who were therefore considered as a subsample. Annual mean concentrations of UFP (as particle number concentration), particulate matter in different particles sizes (PM10, PMcoarse, PM2.5, PM2.5 absorbance), ozone (O3), and nitrogen oxides (NO2, NOX) were estimated by land-use regression models and assigned to participants' home addresses. We performed a multiple linear regression between each pollutant and each biomarker with adjustment for confounders. Per 1 interquartile range (IQR, 1945 particles/cm3) increase of UFP, fibrinogen increased by 0.70 % (0.04; 1.37) and hs-CRP increased by 3.16 % (-0.52; 6.98). Adiponectin decreased by -2.53 % (-4.78; -0.24) per 1 IQR (1.4 µg/m3) increase of PM2.5. Besides, PM2.5 was associated with increased IL-6 in the subsample. In conclusion, we observed that long-term exposure to air pollutants, including both fine and ultrafine particles, was associated with higher concentrations of pro-inflammatory and lower concentrations of an anti-inflammatory blood biomarkers, which is consistent with an increased risk for cardiovascular disease observed for long-term exposure to air pollutants.

The Berlin-Brandenburg Air Study-A Methodological Study Paper of a Natural Experiment Investigating Health Effects Related to Changes in Airport-Related Exposures.

Objectives: This paper presents the study design of the Berlin-Brandenburg Air study (BEAR-study). We measure air quality in Berlin and Brandenburg before and after the relocation of aircraft (AC) traffic from Tegel (TXL) airport to the new Berlin-Brandenburg airport (BER) and investigate the association of AC-related ultrafine particles (UFP) with health outcomes in schoolchildren. Methods: The BEAR-study is a natural experiment examining schoolchildren attending schools near TXL and BER airports, and in control areas (CA) away from both airports and associated air corridors. Each child undergoes repeated school-based health-examinations. Total particle number concentration (PNC) and meteorological parameters are continuously monitored. Submicrometer particle number size distribution, equivalent black carbon, and gas-phase pollutants are collected from long-term air quality monitoring stations. Daily source-specific UFP concentrations are modeled. We will analyze short-term effects of UFP on respiratory, cardiovascular, and neurocognitive outcomes, as well as medium and long-term effects on lung growth and cognitive development. Results: We examined 1,070 children (as of 30 November 2022) from 16 schools in Berlin and Brandenburg. Conclusion: The BEAR study increases the understanding of how AC-related UFP affect children's health.

Impact of ultrafine particles and total particle number concentration on five cause-specific hospital admission endpoints in three German cities.

INTRODUCTION: Numerous studies have shown associations between daily concentrations of fine particles (e.g., particulate matter with an aerodynamic diameter ≤2.5 µm; PM2.5) and morbidity. However, evidence for ultrafine particles (UFP; particles with an aerodynamic diameter of 10-100 nm) remains conflicting. Therefore, we aimed to examine the short-term associations of UFP with five cause-specific hospital admission endpoints for Leipzig, Dresden, and Augsburg, Germany. MATERIAL AND METHODS: We obtained daily counts of (cause-specific) cardiorespiratory hospital admissions between 2010 and 2017. Daily average concentrations of UFP, total particle number (PNC; 10-800 nm), and black carbon (BC) were measured at six sites; PM2.5 and nitrogen dioxide (NO2) were obtained from monitoring networks. We assessed immediate (lag 0-1), delayed (lag 2-4, lag 5-7), and cumulative (lag 0-7) effects by applying station-specific confounder-adjusted Poisson regression models. We then used a novel multi-level meta-analytical method to obtain pooled risk estimates. Finally, we performed two-pollutant models to investigate interdependencies between pollutants and examined possible effect modification by age, sex, and season. RESULTS: UFP showed a delayed (lag 2-4) increase in respiratory hospital admissions of 0.69% [95% confidence interval (CI): -0.28%; 1.67%]. For other hospital admission endpoints, we found only suggestive results. Larger particle size fractions, such as accumulation mode particles (particles with an aerodynamic diameter of 100-800 nm), generally showed stronger effects (respiratory hospital admissions & lag 2-4: 1.55% [95% CI: 0.86%; 2.25%]). PM2.5 showed the most consistent associations for (cardio-)respiratory hospital admissions, whereas NO2 did not show any associations. Two-pollutant models showed independent effects of PM2.5 and BC. Moreover, higher risks have been observed for children. CONCLUSIONS: We observed clear associations with PM2.5 but UFP or PNC did not show a clear association across different exposure windows and cause-specific hospital admissions. Further multi-center studies are needed using harmonized UFP measurements to draw definite conclusions on the health effects of UFP.

Impact of Ambient Ultrafine Particles on Cause-Specific Mortality in Three German Cities.

Rationale: Exposure to ambient air pollution has been associated with adverse effects on morbidity and mortality. However, the evidence for ultrafine particles (UFPs; 10-100 nm) based on epidemiological studies remains scarce and inconsistent. Objectives: We examined associations between short-term exposures to UFPs and total particle number concentrations (PNCs; 10-800 nm) and cause-specific mortality in three German cities: Dresden, Leipzig, and Augsburg. Methods: We obtained daily counts of natural, cardiovascular, and respiratory mortality between 2010 and 2017. UFPs and PNCs were measured at six sites, and measurements of fine particulate matter (PM2.5; ⩽2.5 µm in aerodynamic diameter) and nitrogen dioxide were collected from routine monitoring. We applied station-specific confounder-adjusted Poisson regression models. We investigated air pollutant effects at aggregated lags (0-1, 2-4, 5-7, and 0-7 d after UFP exposure) and used a novel multilevel meta-analytical method to pool the results. Additionally, we assessed interdependencies between pollutants using two-pollutant models. Measurements and Main Results: For respiratory mortality, we found a delayed increase in relative risk of 4.46% (95% confidence interval, 1.52 to 7.48%) per 3,223-particles/cm3 increment 5-7 days after UFP exposure. Effects for PNCs showed smaller but comparable estimates consistent with the observation that the smallest UFP fractions showed the largest effects. No clear associations were found for cardiovascular or natural mortality. UFP effects were independent of PM2.5 in two-pollutant models. Conclusions: We found delayed effects for respiratory mortality within 1 week after exposure to UFPs and PNCs but no associations for natural or cardiovascular mortality. This finding adds to the evidence on the independent health effects of UFPs.

Utility of outdoor central site monitoring in assessing exposure of school children to ultrafine particles.

Epidemiological studies investigating the association between daily particle exposure and health effects are frequently based on a single monitoring site located in an urban background. Using a central site in epidemiological time-series studies has been established based on the premises of low spatial variability of particles within the areas of interest and hence the adequacy of the central sites to monitor the exposure. This is true to a large extent in relation to larger particles (PM2.5, PM10) that are typically monitored and regulated. However, the distribution of ultrafine particles (UFP), which in cities predominantly originate from traffic, is heterogeneous. With increasing pressure to improve the epidemiology of UFP, an important question to ask is, whether central site monitoring is representative of community exposure to this size fraction of particulate matter; addressing this question is the aim of this paper. To achieve this aim, we measured personal exposure to UFP, expressed as particle number concentration (PNC), using Philips Aerasense Nanotracers (NT) carried by the participants of the study, and condensation particle counters (CPC) or scanning mobility particle sizers (SMPS) at central fixed-site monitoring stations. The measurements were conducted at three locations in Brisbane (Australia), Cassino (Italy) and Accra (Ghana). We then used paired t-tests to compare the average personal and average fixed-site PNC measured over the same 24-h, and hourly, periods. We found that, at all three locations, the 24-h average fixed-site PNC was no different to the personal PNC, when averaged over the study period and all the participants. However, the corresponding hourly averages were significantly different at certain times of the day. These were generally times spent commuting and during cooking and eating at home. Our analysis of the data obtained in Brisbane, showed that maximum personal exposure occurred in the home microenvironment during morning breakfast and evening dinner time. The main source of PNC for personal exposure was from the home-microenvironment. We conclude that the 24-h average PNC from the central-site can be used to estimate the 24-h average personal exposure for a community. However, the hourly average PNC from the central site cannot consistently be used to estimate hourly average personal exposure, mainly because they are affected by very different sources.

Levels and drivers of urban black carbon and health risk assessment during pre- and COVID19 lockdown in Augsburg, Germany.

This study aimed to evaluate the levels and phenomenology of equivalent black carbon (eBC) at the city center of Augsburg, Germany (01/2018 to 12/2020). Furthermore, the potential health risk of eBC based on equivalent numbers of passively smoked cigarettes (PSC) was also evaluated, with special emphasis on the impact caused by the COVID19 lockdown restriction measures. As it could be expected, peak concentrations of eBC were commonly recorded in morning (06:00-8:00 LT) and night (19:00-22:00 LT) in all seasons, coinciding with traffic rush hours and atmospheric stagnation. The variability of eBC was highly influenced by diurnal variations in traffic and meteorology (air temperature (T), mixing-layer height (MLH), wind speed (WS)) across days and seasons. Furthermore, a marked "weekend effect" was evidenced, with an average eBC decrease of ∼35% due to lower traffic flow. During the COVID19 lockdown period, an average ∼60% reduction of the traffic flow resulted in ∼30% eBC decrease, as the health risks of eBC exposure was markedly reduced during this period. The implementation of a multilinear regression analysis allowed to explain for 53% of the variability in measured eBC, indicating that the several factors (e.g., traffic and meteorology) may contribute simultaneously to this proportion. Overall, this study will provide valuable input to the policy makers to mitigate eBC pollutant and its adverse effect on environment and human health.

Investigation of COVID-19-related lockdowns on the air pollution changes in augsburg in 2020, Germany.

The COVID-19 pandemic in Germany in 2020 brought many regulations to impede its transmission such as lockdown. Hence, in this study, we compared the annual air pollutants (CO, NO, NO2, O3, PM10, PM2.5, and BC) in Augsburg in 2020 to the record data in 2010-2019. The annual air pollutants in 2020 were significantly (p < 0.001) lower than that in 2010-2019 except O3, which was significantly (p = 0.02) higher than that in 2010-2019. In a depth perspective, we explored how lockdown impacted air pollutants in Augsburg. We simulated air pollutants based on the meteorological data, traffic density, and weekday and weekend/holiday by using four different models (i.e. Random Forest, K-nearest Neighbors, Linear Regression, and Lasso Regression). According to the best fitting effects, Random Forest was used to predict air pollutants during two lockdown periods (16/03/2020-19/04/2020, 1st lockdown and 02/11/2020-31/12/2020, 2nd lockdown) to explore how lockdown measures impacted air pollutants. Compared to the predicted values, the measured CO, NO2, and BC significantly reduced 18.21%, 21.75%, and 48.92% in the 1st lockdown as well as 7.67%, 32.28%, and 79.08% in the 2nd lockdown. It could be owing to the reduction of traffic and industrial activities. O3 significantly increased 15.62% in the 1st lockdown but decreased 40.39% in the 2nd lockdown, which may have relations with the fluctuations the NO titration effect and photochemistry effect. PM10 and PM2.5 were significantly increased 18.23% an 10.06% in the 1st lockdown but reduced 34.37% and 30.62% in the 2nd lockdown, which could be owing to their complex generation mechanisms.

Gaseous air pollutants and DNA methylation in a methylome-wide association study of an ethnically and environmentally diverse population of U.S. adults.

Epigenetic mechanisms may underlie air pollution-health outcome associations. We estimated gaseous air pollutant-DNA methylation (DNAm) associations using twelve subpopulations within Women's Health Initiative (WHI) and Atherosclerosis Risk in Communities (ARIC) cohorts (n = 8397; mean age 61.3 years; 83% female; 46% African-American, 46% European-American, 8% Hispanic/Latino). We used geocoded participant address-specific mean ambient carbon monoxide (CO), nitrogen oxides (NO2; NOx), ozone (O3), and sulfur dioxide (SO2) concentrations estimated over the 2-, 7-, 28-, and 365-day periods before collection of blood samples used to generate Illumina 450 k array leukocyte DNAm measurements. We estimated methylome-wide, subpopulation- and race/ethnicity-stratified pollutant-DNAm associations in multi-level, linear mixed-effects models adjusted for sociodemographic, behavioral, meteorological, and technical covariates. We combined stratum-specific estimates in inverse variance-weighted meta-analyses and characterized significant associations (false discovery rate; FDR<0.05) at Cytosine-phosphate-Guanine (CpG) sites without among-strata heterogeneity (PCochran's Q > 0.05). We attempted replication in the Cooperative Health Research in Region of Augsburg (KORA) study and Normative Aging Study (NAS). We observed a -0.3 (95% CI: -0.4, -0.2) unit decrease in percent DNAm per interquartile range (IQR, 7.3 ppb) increase in 28-day mean NO2 concentration at cg01885635 (chromosome 3; regulatory region 290 bp upstream from ZNF621; FDR = 0.03). At intragenic sites cg21849932 (chromosome 20; LIME1; intron 3) and cg05353869 (chromosome 11; KLHL35; exon 2), we observed a -0.3 (95% CI: -0.4, -0.2) unit decrease (FDR = 0.04) and a 1.2 (95% CI: 0.7, 1.7) unit increase (FDR = 0.04), respectively, in percent DNAm per IQR (17.6 ppb) increase in 7-day mean ozone concentration. Results were not fully replicated in KORA and NAS. We identified three CpG sites potentially susceptible to gaseous air pollution-induced DNAm changes near genes relevant for cardiovascular and lung disease. Further harmonized investigations with a range of gaseous pollutants and averaging durations are needed to determine the effect of gaseous air pollutants on DNA methylation and ultimately gene expression.

A randomization-based causal inference framework for uncovering environmental exposure effects on human gut microbiota.

Statistical analysis of microbial genomic data within epidemiological cohort studies holds the promise to assess the influence of environmental exposures on both the host and the host-associated microbiome. However, the observational character of prospective cohort data and the intricate characteristics of microbiome data make it challenging to discover causal associations between environment and microbiome. Here, we introduce a causal inference framework based on the Rubin Causal Model that can help scientists to investigate such environment-host microbiome relationships, to capitalize on existing, possibly powerful, test statistics, and test plausible sharp null hypotheses. Using data from the German KORA cohort study, we illustrate our framework by designing two hypothetical randomized experiments with interventions of (i) air pollution reduction and (ii) smoking prevention. We study the effects of these interventions on the human gut microbiome by testing shifts in microbial diversity, changes in individual microbial abundances, and microbial network wiring between groups of matched subjects via randomization-based inference. In the smoking prevention scenario, we identify a small interconnected group of taxa worth further scrutiny, including Christensenellaceae and Ruminococcaceae genera, that have been previously associated with blood metabolite changes. These findings demonstrate that our framework may uncover potentially causal links between environmental exposure and the gut microbiome from observational data. We anticipate the present statistical framework to be a good starting point for further discoveries on the role of the gut microbiome in environmental health.

Low emission zones reduced PM10 but not NO2 concentrations in Berlin and Munich, Germany.

Low emission zones (LEZs) aiming at improving the air quality in urban areas have been implemented in many European cities. However, studies are limited in evaluating the effects of LEZ, and most of which used simple methods. In this study, a general additive mixed model was utilized to account for confounders in the atmosphere and validate the effects of LEZ on PM10 and NO2 concentrations in two German cities. In addition, the effects of LEZ on elemental carbon (EC) and total carbon (TC) in Berlin were also evaluated. The LEZ effects were estimated after taking into account air pollutant concentrations at a reference site located in the regional background, and adjusting for hour of the week, public holidays, season, and wind direction. The LEZ in Berlin, and the LEZ in combination with the heavy-duty vehicle (HDV) transit ban in Munich significantly reduced the PM10 concentrations, at both traffic sites (TS) and urban background sites (UB). The effects were greater in LEZ stage 3 than in LEZ stages 2 and 1. Moreover, compared with PM10, LEZ was more efficient in reducing EC, a component that is considered more toxic than PM10 mass. In contrast, the LEZ had no consistent effect on NO2 levels: no effects were observed in Berlin; in Munich, the combination of the LEZ and the HDV transit ban reduced NO2 at UB site in LEZ stage 1, but without further reductions in subsequent stages of the LEZ. Overall, our study indicated that LEZs, which target the major primary air pollution source in the highly populated city center could be an effective way to improve urban air quality such as PM mass concentration and EC level.

A global observational analysis to understand changes in air quality during exceptionally low anthropogenic emission conditions.

This global study, which has been coordinated by the World Meteorological Organization Global Atmospheric Watch (WMO/GAW) programme, aims to understand the behaviour of key air pollutant species during the COVID-19 pandemic period of exceptionally low emissions across the globe. We investigated the effects of the differences in both emissions and regional and local meteorology in 2020 compared with the period 2015-2019. By adopting a globally consistent approach, this comprehensive observational analysis focuses on changes in air quality in and around cities across the globe for the following air pollutants PM2.5, PM10, PMC (coarse fraction of PM), NO2, SO2, NOx, CO, O3 and the total gaseous oxidant (OX = NO2 + O3) during the pre-lockdown, partial lockdown, full lockdown and two relaxation periods spanning from January to September 2020. The analysis is based on in situ ground-based air quality observations at over 540 traffic, background and rural stations, from 63 cities and covering 25 countries over seven geographical regions of the world. Anomalies in the air pollutant concentrations (increases or decreases during 2020 periods compared to equivalent 2015-2019 periods) were calculated and the possible effects of meteorological conditions were analysed by computing anomalies from ERA5 reanalyses and local observations for these periods. We observed a positive correlation between the reductions in NO2 and NOx concentrations and peoples' mobility for most cities. A correlation between PMC and mobility changes was also seen for some Asian and South American cities. A clear signal was not observed for other pollutants, suggesting that sources besides vehicular emissions also substantially contributed to the change in air quality. As a global and regional overview of the changes in ambient concentrations of key air quality species, we observed decreases of up to about 70% in mean NO2 and between 30% and 40% in mean PM2.5 concentrations over 2020 full lockdown compared to the same period in 2015-2019. However, PM2.5 exhibited complex signals, even within the same region, with increases in some Spanish cities, attributed mainly to the long-range transport of African dust and/or biomass burning (corroborated with the analysis of NO2/CO ratio). Some Chinese cities showed similar increases in PM2.5 during the lockdown periods, but in this case, it was likely due to secondary PM formation. Changes in O3 concentrations were highly heterogeneous, with no overall change or small increases (as in the case of Europe), and positive anomalies of 25% and 30% in East Asia and South America, respectively, with Colombia showing the largest positive anomaly of ~70%. The SO2 anomalies were negative for 2020 compared to 2015-2019 (between ~25 to 60%) for all regions. For CO, negative anomalies were observed for all regions with the largest decrease for South America of up to ~40%. The NO2/CO ratio indicated that specific sites (such as those in Spanish cities) were affected by biomass burning plumes, which outweighed the NO2 decrease due to the general reduction in mobility (ratio of ~60%). Analysis of the total oxidant (OX = NO2 + O3) showed that primary NO2 emissions at urban locations were greater than the O3 production, whereas at background sites, OX was mostly driven by the regional contributions rather than local NO2 and O3 concentrations. The present study clearly highlights the importance of meteorology and episodic contributions (e.g., from dust, domestic, agricultural biomass burning and crop fertilizing) when analysing air quality in and around cities even during large emissions reductions. There is still the need to better understand how the chemical responses of secondary pollutants to emission change under complex meteorological conditions, along with climate change and socio-economic drivers may affect future air quality. The implications for regional and global policies are also significant, as our study clearly indicates that PM2.5 concentrations would not likely meet the World Health Organization guidelines in many parts of the world, despite the drastic reductions in mobility. Consequently, revisions of air quality regulation (e.g., the Gothenburg Protocol) with more ambitious targets that are specific to the different regions of the world may well be required.

Longitudinal associations between ambient air pollution and insulin sensitivity: results from the KORA cohort study.

BACKGROUND: Impaired insulin sensitivity could be an intermediate step that links exposure to air pollution to the development of type 2 diabetes. However, longitudinal associations of air pollution with insulin sensitivity remain unclear. Our study investigated the associations of long-term air pollution exposure with the degree and rate of change of insulin sensitivity. METHODS: In this longitudinal study, we analysed data from the Cooperative Health Research in the Region of Augsburg (KORA) cohort from Augsburg, Germany, which recruited participants aged 25-74 years in the survey between 1999 and 2001 (KORA S4), with two follow-up examinations in 2006-08 (KORA F4) and 2013-14 (KORA FF4). Serum concentrations of fasting insulin and glucose, and homoeostasis model assessment of insulin resistance (HOMA-IR, a surrogate measure of insulin sensitivity) and ß-cell function (HOMA-B, a surrogate marker for fasting insulin secretion) were assessed at up to three visits between 1999 and 2014. Annual average air pollutant concentrations at the residence were estimated by land-use regression models. We examined the associations of air pollution with repeatedly assessed biomarker levels using mixed-effects models, and we assessed the associations with the annual rate of change in biomarkers using quantile regression models. FINDINGS: Among 9620 observations from 4261 participants in the KORA cohort, we included 6008 (62·5%) observations from 3297 (77·4%) participants in our analyses. Per IQR increment in annual average air pollutant concentrations, HOMA-IR significantly increased by 2·5% (95% CI 0·3 to 4·7) for coarse particulate matter, by 3·1% (0·9 to 5·3) for PM2·5, by 3·6% (1·0 to 6·3) for PM2·5absorbance, and by 3·2% (0·6 to 5·8) for nitrogen dioxide, and borderline significantly increased by 2·2% (-0·1 to 4·5) for ozone, whereas it did not significantly increase for the whole range of ultrafine particles. Similar positive associations in slightly smaller magnitude were observed for HOMA-B and fasting insulin levels. In addition, air pollutant concentrations were positively associated with the annual rate of change in HOMA-IR, HOMA-B, and fasting insulin. Neither the level nor the rate of change of fasting glucose were associated with air pollution exposure. INTERPRETATION: Our study indicates that long-term air pollution exposure could contribute to the development of insulin resistance, which is one of the key factors in the pathogenesis of type 2 diabetes. FUNDING: German Federal Ministry of Education and Research.

Spatiotemporal Characteristics and Driving Factors of Black Carbon in Augsburg, Germany: Combination of Mobile Monitoring and Street View Images.

The study investigates the spatial pattern of black carbon (BC) at a high spatial resolution in Augsburg, Germany. Sixty two walks were performed to assess the concentrations of equivalent black carbon (eBC), ultraviolet particulate matter (UVPM), and equivalent brown carbon (eBrC) in different seasons and at different times of the day with a mobile platform (i.e., trolley). Along with BC measurements, images of street microenvironments were recorded. Meteorological parameters, including temperature, relative humidity, and wind speed, were monitored. The BC concentrations showed significant spatial heterogeneity and diurnal variations peaking in the morning and at night. The highest BC concentrations were observed near dense traffic. The correlations between BC and street views (buildings, roads, cars, and vegetation) were weak but highly significant. Moreover, meteorological factors also influenced the BC concentration. A model based on street view images and meteorological data was developed to examine the driving factors of the spatial variability of BC concentrations at a higher spatial resolution as different microenvironments based on traffic density. The best results were obtained for UVPM and eBC (71 and 70% explained variability). eBrC (53%), to which other sources besides road traffic can also make significant contributions, is modeled less well.

Long-term trends in PM2.5 mass and particle number concentrations in urban air: The impacts of mitigation measures and extreme events due to changing climates.

Urbanisation and industrialisation led to the increase of ambient particulate matter (PM) concentration. While subsequent regulations may have resulted in the decrease of some PM matrices, the simultaneous changes in climate affecting local meteorological conditions could also have played a role. To gain an insight into this complex matter, this study investigated the long-term trends of two important matrices, the particle mass (PM2.5) and particle number concentrations (PNC), and the factors that influenced the trends. Mann-Kendall test, Sen's slope estimator, the generalised additive model, seasonal decomposition of time series by LOESS (locally estimated scatterplot smoothing) and the Buishand range test were applied. Both PM2.5 and PNC showed significant negative monotonic trends (0.03-0.6 µg m-3. yr-1 and 0.40-3.8 × 103 particles. cm-3. yr-1, respectively) except Brisbane (+0.1 µg m-3. yr-1 and +53 particles. cm-3. yr-1, respectively). For the period covered in this study, temperature increased (0.03-0.07 °C.yr-1) in all cities except London; precipitation decreased (0.02-1.4 mm. yr-1) except in Helsinki; and wind speed was reduced in Brisbane and Rochester but increased in Helsinki, London and Augsburg. At the change-points, temperature increase in cold cities influenced PNC while shifts in precipitation and wind speed affected PM2.5. Based on the LOESS trend, extreme events such as dust storms and wildfires resulting from changing climates caused a positive step-change in concentrations, particularly for PM2.5. In contrast, among the mitigation measures, controlling sulphur in fuels caused a negative step-change, especially for PNC. Policies regarding traffic and fleet management (e.g. low emission zones) that were implemented only in certain areas or in a progressive uptake (e.g. Euro emission standards), resulted to gradual reductions in concentrations. Therefore, as this study has clearly shown that PM2.5 and PNC were influenced differently by the impacts of the changing climate and by the mitigation measures, both metrics must be considered in urban air quality management.

Effect of NOx and NO2 Concentration Increase in Ambient Air to Daily Bronchitis and Asthma Exacerbation, Silesian Voivodeship in Poland.

There is a discussion in Europe about the dominant role of air pollution for health effects, most researchers claim that the particulate matter is responsible for inflammatory processes in the respiratory system, while others underline the role of nitrogen dioxide. The aim of the study was to assess the risk related to NOx, NO2 and PM2.5 concentration increase and daily outpatient visits or hospitalization due to bronchitis and asthma exacerbation in the entire population of Silesian Voivodeship, Poland. To assess the relationship between daily pollutants concentrations and the number of outpatient visits or hospitalizations due to bronchitis and asthma (available in the regional registry), the multivariable log-linear Poisson regression model was used. Results were presented by relative risk (RR) of health outcomes related to the increase in pollutant concentration by unit (interquartile range). Obtained results confirmed a statistically significant association between outpatient visits and hospitalizations due to bronchitis and asthma exacerbation and daily nitrogen oxides concentrations in Silesian voivodeship, Poland. The strongest relationship was observed in the case of NO2 and outpatient visits due to bronchitis, e.g., RR = 1.434 (1.308-1.571) for exposure expressed by the 50-day moving average concentration. In the case of hospitalizations, the health effect was lagged a few days in relation to the increase in exposure.

Hourly Exposure to Ultrafine Particle Metrics and the Onset of Myocardial Infarction in Augsburg, Germany.

BACKGROUND: Epidemiological evidence on the health effects of ultrafine particles (UFP) remains insufficient to infer a causal relationship that is largely due to different size ranges and exposure metrics examined across studies. Moreover, evidence regarding the association between UFP and cardiovascular disease at a sub-daily timescale is lacking. OBJECTIVE: We investigated the relationship between different particle metrics, including particle number (PNC), length (PLC), and surface area (PSC) concentrations, and myocardial infarction (MI) at an hourly timescale. METHODS: We collected hourly air pollution and meteorological data from fixed urban background monitoring sites and hourly nonfatal MI cases from a MI registry in Augsburg, Germany, during 2005-2015. We conducted a time-stratified case-crossover analysis with conditional logistic regression to estimate the association between hourly particle metrics and MI cases, adjusted for air temperature and relative humidity. We also examined the independent effects of a certain particle metric in two-pollutant models by adjusting for copollutants, including particulate matter (PM) with an aerodynamic diameter of ≤10µm or 2.5µm (PM10 and PM2.5, respectively), nitrogen dioxide, ozone, and black carbon. RESULTS: Overall, a total of 5,898 cases of nonfatal MI cases were recorded. Exploratory analyses showed similar associations across particle metrics in the first 6-12 h. For example, interquartile range increases in PNC within the size range of 10-100 nm, PLC, and PSC were associated with an increase of MI 6 h later by 3.27% [95% confidence interval (CI): 0.27, 6.37], 5.71% (95% CI: 1.79, 9.77), and 5.84% (95% CI: 1.04, 10.87), respectively. Positive, albeit imprecise, associations were observed for PNC within the size range of 10-30 nm and 100-500 nm. Effect estimates for PLC and PSC remained similar after adjustment for PM and gaseous pollutants. CONCLUSIONS: Transient exposure to particle number, length, and surface area concentrations or other potentially related exposures may trigger the onset of nonfatal myocardial infraction. https://doi.org/10.1289/EHP5478.

Methylome-wide association study provides evidence of particulate matter air pollution-associated DNA methylation.

BACKGROUND: DNA methylation (DNAm) may contribute to processes that underlie associations between air pollution and poor health. Therefore, our objective was to evaluate associations between DNAm and ambient concentrations of particulate matter (PM) ≤2.5, ≤10, and 2.5-10 µm in diameter (PM2.5; PM10; PM2.5-10). METHODS: We conducted a methylome-wide association study among twelve cohort- and race/ethnicity-stratified subpopulations from the Women's Health Initiative and the Atherosclerosis Risk in Communities study (n = 8397; mean age: 61.5 years; 83% female; 45% African American; 9% Hispanic/Latino American). We averaged geocoded address-specific estimates of daily and monthly mean PM concentrations over 2, 7, 28, and 365 days and 1 and 12 months before exams at which we measured leukocyte DNAm in whole blood. We estimated subpopulation-specific, DNAm-PM associations at approximately 485,000 Cytosine-phosphate-Guanine (CpG) sites in multi-level, linear, mixed-effects models. We combined subpopulation- and site-specific estimates in fixed-effects, inverse variance-weighted meta-analyses, then for associations that exceeded methylome-wide significance and were not heterogeneous across subpopulations (P < 1.0 × 10-7; PCochran's Q > 0.10), we characterized associations using publicly accessible genomic databases and attempted replication in the Cooperative Health Research in the Region of Augsburg (KORA) study. RESULTS: Analyses identified significant DNAm-PM associations at three CpG sites. Twenty-eight-day mean PM10 was positively associated with DNAm at cg19004594 (chromosome 20; MATN4; P = 3.33 × 10-8). One-month mean PM10 and PM2.5-10 were positively associated with DNAm at cg24102420 (chromosome 10; ARPP21; P = 5.84 × 10-8) and inversely associated with DNAm at cg12124767 (chromosome 7; CFTR; P = 9.86 × 10-8). The PM-sensitive CpG sites mapped to neurological, pulmonary, endocrine, and cardiovascular disease-related genes, but DNAm at those sites was not associated with gene expression in blood cells and did not replicate in KORA. CONCLUSIONS: Ambient PM concentrations were associated with DNAm at genomic regions potentially related to poor health among racially, ethnically and environmentally diverse populations of U.S. women and men. Further investigation is warranted to uncover mechanisms through which PM-induced epigenomic changes may cause disease.

Ultrafine particles and PM2.5 in the air of cities around the world: Are they representative of each other?

Can mitigating only particle mass, as the existing air quality measures do, ultimately lead to reduction in ultrafine particles (UFP)? The aim of this study was to provide a broader urban perspective on the relationship between UFP, measured in terms of particle number concentration (PNC) and PM2.5 (mass concentration of particles with aerodynamic diameter < 2.5 µm) and factors that influence their concentrations. Hourly average PNC and PM2.5 were acquired from 10 cities located in North America, Europe, Asia, and Australia over a 12-month period. A pairwise comparison of the mean difference and the Kolmogorov-Smirnov test with the application of bootstrapping were performed for each city. Diurnal and seasonal trends were obtained using a generalized additive model (GAM). The particle number to mass concentration ratios and the Pearson's correlation coefficient were calculated to elucidate the nature of the relationship between these two metrics. Results show that the annual mean concentrations ranged from 8.0 × 103 to 19.5 × 103 particles·cm-3 and from 7.0 to 65.8 µg·m-3 for PNC and PM2.5, respectively, with the data distributions generally skewed to the right, and with a wider spread for PNC. PNC showed a more distinct diurnal trend compared with PM2.5, attributed to the high contributions of UFP from vehicular emissions to PNC. The variation in both PNC and PM2.5 due to seasonality is linked to the cities' geographical location and features. Clustering the cities based on annual median concentrations of both PNC and PM2.5 demonstrated that a high PNC level does not lead to a high PM2.5, and vice versa. The particle number-to-mass ratio (in units of 109 particles·µg-1) ranged from 0.14 to 2.2, >1 for roadside sites and <1 for urban background sites with lower values for more polluted cities. The Pearson's r ranged from 0.09 to 0.64 for the log-transformed data, indicating generally poor linear correlation between PNC and PM2.5. Therefore, PNC and PM2.5 measurements are not representative of each other; and regulating PM2.5 does little to reduce PNC. This highlights the need to establish regulatory approaches and control measures to address the impacts of elevated UFP concentrations, especially in urban areas, considering their potential health risks.

The Relationship between Daily Concentration of Fine Particulate Matter in Ambient Air and Exacerbation of Respiratory Diseases in Silesian Agglomeration, Poland.

The relationship between the worsening of air quality during the colder season of the year and respiratory health problems among the exposed population in many countries located in cold climates has been well documented in numerous studies. Silesian Voivodeship, a region located in southern Poland, is one of the most polluted regions in Europe. The aim of this study was to assess the relationship between daily concentration of particulate matter (PM: PM2.5 and PM10) in ambient air and exacerbations of respiratory diseases during the period from 1 January 2016 to 31 August 2017 in the central agglomeration area of Silesian Voivodeship. The study results confirmed a significant increase of daily fine particulate matter concentration in ambient air during the cold season in Silesian Voivodeship with a simultaneous increase of the number of outpatient visits and hospitalizations due to respiratory diseases. The moving average concentration was better suited for the modelling of biological response as a result of PM2.5 or PM10 exposure than the temporal lag of health effects. Each increase of dose expressed in the form of moving average concentration over a longer time leads to an increase in the daily number of respiratory effects. The highest risk of hospitalization due to respiratory diseases was related to longer exposure of PM expressed by two to four weeks of exposure; outpatient visits was related to a shorter exposure duration of 3 days.

Ambient and controlled exposures to particulate air pollution and acute changes in heart rate variability and repolarization.

Previous studies have reported increased risks of myocardial infarction in association with elevated ambient particulate matter (PM) in the previous hour(s). However, whether PM can trigger mechanisms that act on this time scale is still unclear. We hypothesized that increases in PM are associated with rapid changes in measures of heart rate variability and repolarization. We used data from panel studies in Augsburg, Germany, and Rochester, New York, USA, and two controlled human exposure studies in Rochester. Data included ECG recordings from all four studies, controlled exposures to (concentrated) ultrafine particles (UFP; particles with an aerodynamic diameter <100 nm) and ambient concentrations of UFP and fine PM (PM2.5, aerodynamic diameter <2.5 µm). Factor analysis identified three representative ECG parameters: standard deviation of NN-intervals (SDNN), root mean square of successive differences (RMSSD), and T-wave complexity. Associations between air pollutants and ECG parameters in the concurrent and previous six hours were estimated using additive mixed models adjusting for long- and short-term time trends, meteorology, and study visit number. We found decreases in SDNN in relation to increased exposures to UFP in the previous five hours in both of the panel studies (e.g. Augsburg study, lag 3 hours: -2.26%, 95% confidence interval [CI]: -3.98% to -0.53%; Rochester panel study, lag 1 hour: -2.69%; 95% CI: -5.13% to -0.26%) and one of the two controlled human exposure studies (1-hour lag: -13.22%; 95% CI: -24.11% to -2.33%). Similarly, we observed consistent decreases in SDNN and RMSSD in association with elevated PM2.5 concentrations in the preceding six hours in both panel studies. We did not find consistent associations between particle metrics and T-wave complexity. This study provided consistent evidence that recent exposures to UFP and PM2.5 can induce acute pathophysiological responses.