•An evaluation of the use of LCZ maps in a local scale urban climate model.•Extensive analysis of temperature regime for each LCZ in our study areas.•Spatially explicit information on hot spots in ...terms of mitigation needs for urban planning.
High population densities in cities and rapid urban growth increase the vulnerability of the urban environment to extreme weather events. Urban planning should account for these extreme events as efficiently as possible. One way is to locate hot spots in an urban environment by mapping cities into local climate zones (LCZ) and evaluate heat stress related to these zones. LCZs are likely to become a standard in urban climate modelling as they capture important urban morphological characteristics. For instance, temperature regimes linked to spatially explicit LCZ maps should be assessed for all LCZ zones derived from these maps. This study assesses the thermal behavior of mapped LCZs using simulated temperature data from the UrbClim model. Prior to temperature analysis, the model was validated with observational data. To evaluate the robustness of the analysis, we ran the model in three cities in Belgium: Antwerp, Brussels, and Ghent. The results show that temperature regimes are significantly different for all the built zones in the urban environment independent of the city. Second, the susceptibility to heat stress can differ greatly depending on the zone. The unique thermal behavior of the different LCZs provides indispensable information on the urban environment and its climatic conditions. This study shows that the LCZ scheme has a potential to help urban planners globally tackle adverse effects of extreme weather events.
Studies on the association between traffic noise and cardiovascular diseases have rarely considered air pollution as a covariate in the analyses. Isolated systolic hypertension has not yet been in ...the focus of epidemiological noise research.
The association between traffic noise (road and rail) and the prevalence of hypertension was assessed in two study populations with a total of 4,166 participants 25-74 years of age. Traffic noise (weighted day-night average noise level; LDN) at the facade of the dwellings was derived from noise maps. Annual average PM2.5 mass concentrations at residential addresses were estimated by land-use regression. Hypertension was assessed by blood pressure readings, self-reported doctor-diagnosed hypertension, and antihypertensive drug intake.
In the Greater Augsburg, Germany, study population, traffic noise and air pollution were not associated with hypertension. In the City of Augsburg population (n = 1,893), where the exposure assessment was more detailed, the adjusted odds ratio (OR) for a 10-dB(A) increase in noise was 1.16 (95% CI: 1.00, 1.35), and 1.11 (95% CI: 0.94, 1.30) after additional adjustment for PM2.5. The adjusted OR for a 1-μg/m3 increase in PM2.5 was 1.15 (95% CI: 1.02, 1.30), and 1.11 (95% CI: 0.98, 1.27) after additional adjustment for noise. For isolated systolic hypertension, the fully adjusted OR for noise was 1.43 (95% CI: 1.10, 1.86) and for PM2.5 was 1.08 (95% CI: 0.87, 1.34).
Traffic noise and PM2.5 were both associated with a higher prevalence of hypertension. Mutually adjusted associations with hypertension were positive but no longer statistically significant.
•Multi-city study with highly standardized particle size distribution measurements.•We applied a novel multilevel model to meta-analyze site-specific results.•No clear associations for ultrafine ...particles (UFP) and cause-specific hospital admissions.•Consistent effects for larger particle size fractions and fine particles (PM2.5).•Higher respiratory hospital admission risk for children, and in the cold season.
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.
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.
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.
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.
Epidemiological studies have reported associations between particulate matter (PM) concentrations and cancer and respiratory and cardiovascular diseases. DNA methylation has been identified as a ...possible link but so far it has only been analyzed in candidate sites.
We studied the association between DNA methylation and short- and mid-term air pollution exposure using genome-wide data and identified potential biological pathways for additional investigation.
We collected whole blood samples from three independent studies-KORA F3 (2004-2005) and F4 (2006-2008) in Germany, and the Normative Aging Study (1999-2007) in the United States-and measured genome-wide DNA methylation proportions with the Illumina 450k BeadChip. PM concentration was measured daily at fixed monitoring stations and three different trailing averages were considered and regressed against DNA methylation: 2-day, 7-day and 28-day. Meta-analysis was performed to pool the study-specific results.
Random-effect meta-analysis revealed 12 CpG (cytosine-guanine dinucleotide) sites as associated with PM concentration (1 for 2-day average, 1 for 7-day, and 10 for 28-day) at a genome-wide Bonferroni significance level (p ≤ 7.5E-8); 9 out of these 12 sites expressed increased methylation. Through estimation of I2 for homogeneity assessment across the studies, 4 of these sites (annotated in NSMAF, C1orf212, MSGN1, NXN) showed p > 0.05 and I2 < 0.5: the site from the 7-day average results and 3 for the 28-day average. Applying false discovery rate, p-value < 0.05 was observed in 8 and 1,819 additional CpGs at 7- and 28-day average PM2.5 exposure respectively.
The PM-related CpG sites found in our study suggest novel plausible systemic pathways linking ambient PM exposure to adverse health effect through variations in DNA methylation.
Panni T, Mehta AJ, Schwartz JD, Baccarelli AA, Just AC, Wolf K, Wahl S, Cyrys J, Kunze S, Strauch K, Waldenberger M, Peters A. 2016. A genome-wide analysis of DNA methylation and fine particulate matter air pollution in three study populations: KORA F3, KORA F4, and the Normative Aging Study. Environ Health Perspect 124:983-990; http://dx.doi.org/10.1289/ehp.1509966.
There is evidence for adverse effects of outdoor air pollution on lung function of children. Quantitative summaries of the effects of air pollution on lung function, however, are lacking due to large ...differences among studies.
We aimed to study the association between residential exposure to air pollution and lung function in five European birth cohorts with a standardized exposure assessment following a common protocol.
As part of the European Study of Cohorts for Air Pollution Effects (ESCAPE) we analyzed data from birth cohort studies situated in Germany, Sweden, the Netherlands, and the United Kingdom that measured lung function at 6-8 years of age (n = 5,921). Annual average exposure to air pollution nitrogen oxides (NO2, NOx), mass concentrations of particulate matter with diameters < 2.5, < 10, and 2.5-10 μm (PM2.5, PM10, and PMcoarse), and PM2.5 absorbance at the birth address and current address was estimated by land-use regression models. Associations of lung function with estimated air pollution levels and traffic indicators were estimated for each cohort using linear regression analysis, and then combined by random effects meta-analysis.
Estimated levels of NO2, NOx, PM2.5 absorbance, and PM2.5 at the current address, but not at the birth address, were associated with small decreases in lung function. For example, changes in forced expiratory volume in 1 sec (FEV1) ranged from -0.86% (95% CI: -1.48, -0.24%) for a 20-μg/m3 increase in NOx to -1.77% (95% CI: -3.34, -0.18%) for a 5-μg/m3 increase in PM2.5.
Exposure to air pollution may result in reduced lung function in schoolchildren.
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.
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.
Display omitted
•Both PM2.5 and PNC had a monotonic downward trend in all cities except Brisbane.•Extreme events due to changing climates caused positive step-changes to PM2.5.•Negative step-changes in PNC were observed upon regulation of sulphur in fuels.•Gradual reduction of PM2.5 and PNC was achieved by traffic and fleet management.
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.
The aim of this study was to determine the effect of six traffic-related air pollution metrics (nitrogen dioxide, nitrogen oxides, particulate matter with an aerodynamic diameter <10 μm (PM10), ...PM2.5, coarse particulate matter and PM2.5 absorbance) on childhood asthma and wheeze prevalence in five European birth cohorts: MAAS (England, UK), BAMSE (Sweden), PIAMA (the Netherlands), GINI and LISA (both Germany, divided into north and south areas). Land-use regression models were developed for each study area and used to estimate outdoor air pollution exposure at the home address of each child. Information on asthma and current wheeze prevalence at the ages of 4-5 and 8-10 years was collected using validated questionnaires. Multiple logistic regression was used to analyse the association between pollutant exposure and asthma within each cohort. Random-effects meta-analyses were used to combine effect estimates from individual cohorts. The meta-analyses showed no significant association between asthma prevalence and air pollution exposure (e.g. adjusted OR (95%CI) for asthma at age 8-10 years and exposure at the birth address (n=10377): 1.10 (0.81-1.49) per 10 μg · m(-3) nitrogen dioxide; 0.88 (0.63-1.24) per 10 μg · m(-3) PM10; 1.23 (0.78-1.95) per 5 μg · m(-3) PM2.5). This result was consistently found in initial crude models, adjusted models and further sensitivity analyses. This study found no significant association between air pollution exposure and childhood asthma prevalence in five European birth cohorts.
PDF
