There is a growing body of evidence that exposure to transportation related noise can adversely affect health and wellbeing. More recently, research on cardiovascular disease has specifically ...explored the hypothesis that exposure to transportation noise increases the risk for ischemic heart disease (IHD). Our objective was to review and conduct a meta-analysis to obtain an overall exposure–response association.
We conducted a systematic review and retained published studies on incident cases of IHD using sources of transportation noise as exposure. Study-specific results were transformed into risk estimates per 10dB increase in exposure. Subsequently we conducted a random effects meta-analysis to pool the estimates. We identified 10 studies on road and aircraft noise exposure conducted since the mid-1990s, providing a total of 12 risk estimates. Pooled relative risk for IHD was 1.06 (1.03–1.09) per 10dB increase in noise exposure with the linear exposure–response starting at 50dB. Based on a small number of studies, subgroup analyses were suggestive of higher risk for IHD for males compared to females (p=0.14), and for persons over 65 years of age compared to under (p=0.22). Air pollution adjustment, explored only in a subset of four studies, did not substantially attenuate the association between noise exposure and IHD.
The evidence for an effect of transportation noise with IHD necessitates further research into the threshold and the shape of the exposure–response association, potential sources of heterogeneity and effect modification. Research in different cultural contexts is also important to derive regional and local estimates for the contribution of transportation noise to the global burden of disease.
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•We review and conduct a meta-analysis on transportation noise exposure and IHD.•Novel approach to pool studies with a diversity of metrics and exposure categories.•We verify the assumption of a linear ER association by targeted statistical analyses.•The overall RR is 1.06 (1.03–1.09) per 10dB increase in noise, starting at 50dB.•More studies are needed to refine the shape and threshold for the ER relationship.
Long-term exposure to ambient air pollution has been associated with premature mortality, but associations at concentrations lower than current annual limit values are uncertain. We analysed ...associations between low-level air pollution and mortality within the multicentre study Effects of Low-Level Air Pollution: A Study in Europe (ELAPSE).
In this multicentre longitudinal study, we analysed seven population-based cohorts of adults (age ≥30 years) within ELAPSE, from Belgium, Denmark, England, the Netherlands, Norway, Rome (Italy), and Switzerland (enrolled in 2000–11; follow-up until 2011–17). Mortality registries were used to extract the underlying cause of death for deceased individuals. Annual average concentrations of fine particulate matter (PM2·5), nitrogen dioxide (NO2), black carbon, and tropospheric warm-season ozone (O3) from Europe-wide land use regression models at 100 m spatial resolution were assigned to baseline residential addresses. We applied cohort-specific Cox proportional hazard models with adjustment for area-level and individual-level covariates to evaluate associations with non-accidental mortality, as the main outcome, and with cardiovascular, non-malignant respiratory, and lung cancer mortality. Subset analyses of participants living at low pollutant concentrations (as per predefined values) and natural splines were used to investigate the concentration-response function. Cohort-specific effect estimates were pooled in a random-effects meta-analysis.
We analysed 28 153 138 participants contributing 257 859 621 person-years of observation, during which 3 593 741 deaths from non-accidental causes occurred. We found significant positive associations between non-accidental mortality and PM2·5, NO2, and black carbon, with a hazard ratio (HR) of 1·053 (95% CI 1·021–1·085) per 5 μg/m3 increment in PM2·5, 1·044 (1·019–1·069) per 10 μg/m3 NO2, and 1·039 (1·018–1·059) per 0·5 × 10−5/m black carbon. Associations with PM2·5, NO2, and black carbon were slightly weaker for cardiovascular mortality, similar for non-malignant respiratory mortality, and stronger for lung cancer mortality. Warm-season O3 was negatively associated with both non-accidental and cause-specific mortality. Associations were stronger at low concentrations: HRs for non-accidental mortality at concentrations lower than the WHO 2005 air quality guideline values for PM2·5 (10 μg/m3) and NO2 (40 μg/m3) were 1·078 (1·046–1·111) per 5 μg/m3 PM2·5 and 1·049 (1·024–1·075) per 10 μg/m3 NO2. Similarly, the association between black carbon and non-accidental mortality was highest at low concentrations, with a HR of 1·061 (1·032–1·092) for exposure lower than 1·5× 10−5/m, and 1·081 (0·966–1·210) for exposure lower than 1·0× 10−5/m.
Long-term exposure to concentrations of PM2·5 and NO2 lower than current annual limit values was associated with non-accidental, cardiovascular, non-malignant respiratory, and lung cancer mortality in seven large European cohorts. Continuing research on the effects of low concentrations of air pollutants is expected to further inform the process of setting air quality standards in Europe and other global regions.
Health Effects Institute.
To investigate the associations between air pollution and mortality, focusing on associations below current European Union, United States, and World Health Organization standards and guidelines.
...Pooled analysis of eight cohorts.
Multicentre project Effects of Low-Level Air Pollution: A Study in Europe (ELAPSE) in six European countries.
325 367 adults from the general population recruited mostly in the 1990s or 2000s with detailed lifestyle data. Stratified Cox proportional hazard models were used to analyse the associations between air pollution and mortality. Western Europe-wide land use regression models were used to characterise residential air pollution concentrations of ambient fine particulate matter (PM
), nitrogen dioxide, ozone, and black carbon.
Deaths due to natural causes and cause specific mortality.
Of 325 367 adults followed-up for an average of 19.5 years, 47 131 deaths were observed. Higher exposure to PM
, nitrogen dioxide, and black carbon was associated with significantly increased risk of almost all outcomes. An increase of 5 µg/m
in PM
was associated with 13% (95% confidence interval 10.6% to 15.5%) increase in natural deaths; the corresponding figure for a 10 µg/m
increase in nitrogen dioxide was 8.6% (7% to 10.2%). Associations with PM
, nitrogen dioxide, and black carbon remained significant at low concentrations. For participants with exposures below the US standard of 12 µg/m
an increase of 5 µg/m
in PM
was associated with 29.6% (14% to 47.4%) increase in natural deaths.
Our study contributes to the evidence that outdoor air pollution is associated with mortality even at low pollution levels below the current European and North American standards and WHO guideline values. These findings are therefore an important contribution to the debate about revision of air quality limits, guidelines, and standards, and future assessments by the Global Burden of Disease.
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•Long-term road traffic and railway noise are associated with most CVD causes of death.•Risk increases often start well below the WHO Environmental Noise guideline ...levels.•Associations are independent of air pollution.•Higher levels of noise intermittency are independently associated with each outcome.•Relative and absolute risk are higher in males compared to females.
Death from cardiovascular diseases (CVD) has been associated with transportation noise. This nationwide cohort, with state-of-the-art exposure assessment, evaluates these associations by noise source.
Road traffic, railway and aircraft noise for 2001 and 2011 were linked to 4.1 million adults in the Swiss National Cohort, accounting for address history. Mean noise exposure in 5-year periods was calculated. Time-varying Cox regression models, with age as timescale, were applied to all and cause-specific cardiovascular causes of death. Models included all three noise sources plus PM2.5, adjusted for individual and spatial covariates. Nighttime noise events for all sources combined (expressed as intermittency ratio or number of events) were considered in sensitivity analyses. Absolute excess risk was calculated by multiplying deaths/100,000 person-years by the excess risk (hazard ratio-1) within each age/sex group.
During a 15-year follow-up, there were 277,506 CVD and 34,200 myocardial infarction (MI) deaths. Associations (hazard ratio; 95%-CIs) for road traffic, railway and aircraft noise and CVD mortality were 1.029 (1.024–1.034), 1.013 (1.010–1.017), and 1.003 (0.996–1.010) per 10 dB Lden, respectively. Associations for MI mortality were a respective 1.043 (1.029–1.058), 1.020 (1.010–1.030) and 1.040 (1.020–1.060) per 10 dB Lden. Blood pressure-related, ischemic heart disease, and all stroke mortality were significantly associated with road traffic and railway noise, while ischemic stroke mortality was associated with aircraft noise. Associations were mostly linear, often starting below 40 dB Lden for road traffic and railway noise. Higher levels of noise intermittency were also independently associated with each outcome. While the absolute number of deaths attributed to noise increased with age, the hazard ratios declined with age. Relative and absolute risk was higher in males compared to females.
Independent of air pollution, transportation noise exposure is associated with all and cause-specific CVD mortality, with effects starting below current guideline limits.
The aim of the present study is to establish exposure-response relationships reflecting the percentage highly annoyed (%HA) as functions of road traffic, railway, and aircraft noise exposure, ...measured as day-evening-night level (Lden), as well as to elucidate the degree to which the acoustic indicator Intermittency Ratio (IR), which reflects the “eventfulness” of a noise situation, predicts noise annoyance. We conducted a mixed-mode representative population survey in a stratified random sample of 5592 residents exposed to transportation noise all over Switzerland. Source-specific noise exposure was calculated for each floor and each façade based on comprehensive traffic data. Noise annoyance was measured using the ICBEN 11-point scale. The survey was carried out in 4 waves at different times of the year. We hypothesized that in addition to Lden, the effects of noise on annoyance can be better explained when also considering the intensity of short-term variations of noise level over time. We therefore incorporated the acoustic indicator IR in the statistical models. For all noise sources, results revealed significant associations between Lden and %HA after controlling for confounders and independent predictors such as IR (measured over 24 h), exposure to other transportation noise sources, sex and age, language, home ownership, education level, living duration, temperature, and access to a quiet side of the dwelling. Aircraft noise annoyance scored markedly higher than annoyance to railway and road traffic noise at the same Lden level. Railway noise elicited higher percentages of highly annoyed persons than road traffic noise. Results furthermore suggest that for road traffic noise, IR has an additional effect on %HA and can explain shifts of the exposure-response curve of up to about 6 dB between low IR and high IR exposure situations, possibly due to the effect of different durations of noise-free intervals between events. For railway and aircraft noise annoyance, the predictive value of IR was limited.
•Noise annoyance is associated with Lden of road, rail, and aircraft noise exposure.•The degree of intermittency of noise can explain differences in annoyance reactions.•Aircraft noise is more annoying than railway noise and road traffic noise.•We found no empirical basis for a “railway bonus” for noise regulation.
In order to investigate associations between air pollution and adverse health effects consistent fine spatial air pollution surfaces are needed across large areas to provide cohorts with comparable ...exposures. The aim of this paper is to develop and evaluate fine spatial scale land use regression models for four major health relevant air pollutants (PM2.5, NO2, BC, O3) across Europe.
We developed West-European land use regression models (LUR) for 2010 estimating annual mean PM2.5, NO2, BC and O3 concentrations (including cold and warm season estimates for O3). The models were based on AirBase routine monitoring data (PM2.5, NO2 and O3) and ESCAPE monitoring data (BC), and incorporated satellite observations, dispersion model estimates, land use and traffic data. Kriging was performed on the residual spatial variation from the LUR models and added to the exposure estimates. One model was developed using all sites (100%). Robustness of the models was evaluated by performing a five-fold hold-out validation and for PM2.5 and NO2 additionally with independent comparison at ESCAPE measurements. To evaluate the stability of each model's spatial structure over time, separate models were developed for different years (NO2 and O3: 2000 and 2005; PM2.5: 2013).
The PM2.5, BC, NO2, O3 annual, O3 warm season and O3 cold season models explained respectively 72%, 54%, 59%, 65%, 69% and 83% of spatial variation in the measured concentrations. Kriging proved an efficient technique to explain a part of residual spatial variation for the pollutants with a strong regional component explaining respectively 10%, 24% and 16% of the R2 in the PM2.5, O3 warm and O3 cold models. Explained variance at fully independent sites vs the internal hold-out validation was slightly lower for PM2.5 (65% vs 66%) and lower for NO2 (49% vs 57%). Predictions from the 2010 model correlated highly with models developed in other years at the overall European scale.
We developed robust PM2.5, NO2, O3 and BC hybrid LUR models. At the West-European scale models were robust in time, becoming less robust at smaller spatial scales. Models were applied to 100 × 100 m surfaces across Western Europe to allow for exposure assignment for 35 million participants from 18 European cohorts participating in the ELAPSE study.
•Robust PM2.5, NO2, BC and O3 hybrid LUR models at a 100x100 m resolution for Western Europe were developed•Models included large scale SAT and CTM estimates and fine scale traffic and land use and were further improved with kriging•Models were robust in time at European scale, becoming less robust at smaller spatial scales.
•Residential green reduces road traffic and railway noise annoyance.•The annoyance reduction is equivalent to 6 dB (road) and 3 dB (rail).•Residential green is associated with increased aircraft ...noise annoyance.•The annoyance increase is equivalent to 10 dB (air).•Quality attributes of residential green depend on the degree of urbanization.
In recent years, residential green and availability of neighbourhood green spaces came into focus as a potential means to reduce transportation noise annoyance. Literature suggests that various characteristics of residential green may play a role, namely, greenness of the residential areas as quantified by the normalized difference vegetation index (NDVI), visible vegetation from home, and the presence of public green spaces as identified by land use classification data (LU-green), as well as their accessibility and noise pollution (i.e., transportation noise exposure within green areas, how loud/quiet they are). So far, studies mostly focused on road traffic noise in urban areas.
We investigated the effects of residential green on noise annoyance, accounting for different transportation noise sources as well as for the degree of urbanisation.
We complemented the data set of the recent Swiss SiRENE survey on road traffic, railway and aircraft noise annoyance with a wide range of “green” metrics, and investigated their association with annoyance by means of logistic regression analysis (generalized estimating equations).
Increasing residential green was found to be associated with reduced road traffic and railway noise annoyance, but increased aircraft noise annoyance. The overall effect corresponded to equivalent level reductions of about 6 dB for road traffic and 3 dB for railway noise, but to an increase of about 10 dB for aircraft noise, when residential green increased from “not much green” (5th percentile of the study sample distribution) to “a lot of green” (95th percentile). Overall, NDVI and LU-green were particularly strongly linked to annoyance. The effects of visible vegetation from home and accessibility and/or quietness of green spaces were, overall, less strong, but depended on the degree of urbanisation. For road traffic noise, visible vegetation and accessibility of green spaces seem to particularly strongly reduce annoyance in cities, while quiet green spaces are more effective in rural areas.
Our study emphasizes that residential green should be fostered by city planners, particularly in densely populated areas.
Empirical spatial air pollution models have been applied extensively to assess exposure in epidemiological studies with increasingly sophisticated and complex statistical algorithms beyond ordinary ...linear regression. However, different algorithms have rarely been compared in terms of their predictive ability.
This study compared 16 algorithms to predict annual average fine particle (PM2.5) and nitrogen dioxide (NO2) concentrations across Europe. The evaluated algorithms included linear stepwise regression, regularization techniques and machine learning methods. Air pollution models were developed based on the 2010 routine monitoring data from the AIRBASE dataset maintained by the European Environmental Agency (543 sites for PM2.5 and 2399 sites for NO2), using satellite observations, dispersion model estimates and land use variables as predictors. We compared the models by performing five-fold cross-validation (CV) and by external validation (EV) using annual average concentrations measured at 416 (PM2.5) and 1396 sites (NO2) from the ESCAPE study. We further assessed the correlations between predictions by each pair of algorithms at the ESCAPE sites.
For PM2.5, the models performed similarly across algorithms with a mean CV R2 of 0.59 and a mean EV R2 of 0.53. Generalized boosted machine, random forest and bagging performed best (CV R2~0.63; EV R2 0.58–0.61), while backward stepwise linear regression, support vector regression and artificial neural network performed less well (CV R2 0.48–0.57; EV R2 0.39–0.46). Most of the PM2.5 model predictions at ESCAPE sites were highly correlated (R2 > 0.85, with the exception of predictions from the artificial neural network). For NO2, the models performed even more similarly across different algorithms, with CV R2s ranging from 0.57 to 0.62, and EV R2s ranging from 0.49 to 0.51. The predicted concentrations from all algorithms at ESCAPE sites were highly correlated (R2 > 0.9). For both pollutants, biases were low for all models except the artificial neural network. Dispersion model estimates and satellite observations were two of the most important predictors for PM2.5 models whilst dispersion model estimates and traffic variables were most important for NO2 models in all algorithms that allow assessment of the importance of variables.
Different statistical algorithms performed similarly when modelling spatial variation in annual average air pollution concentrations using a large number of training sites.
•Multiple statistical algorithms with very different assumptions were compared.•Despite the difference in modeling frameworks, predictions among the models exhibit generally good agreement.•The use of an external evaluation dataset strengthens evaluation by cross-validation.
•Exploring the effects of long-term exposure to air pollution on dementia incidence.•PM2.5 level was associated with Alzheimer’s disease and vascular dementia incidence.•No association was detected ...between NO2 or black carbon exposure and dementia risk.•PM2.5 might be a modifiable risk factor of the main forms of dementia.
Emerging epidemiological evidence suggests a relationship between exposure to air pollution and dementia. However, most of the existing studies relied on health administrative databases for the diagnosis of dementia. In a large French population-based cohort (the 3C Study), we assessed the effects of particulate matter ≤2.5 µm (PM2.5), nitrogen dioxide (NO2) and black carbon (BC) on the risk of dementia diagnosed with reliable tools.
Participants aged ≥65 years were recruited between 1999 and 2001 and followed for 12 years. At baseline and every 2 years, dementia was suspected on the basis of the neuropsychological and neurological examination and confirmed by an independent committee of clinicians. Exposure to NO2, BC and PM2.5 at the participants’ residential address was estimated using land use regression models. For each pollutant and year of follow-up, the 10-year moving average of past exposure was estimated. Multilevel spatial random-effects Cox proportional hazards models were used in which exposure was included as a time-varying variable. Analyses were adjusted for individual (age, sex, education, APOE4 genotype, health behaviours) and contextual (neighbourhood deprivation index) confounders.
At baseline, the median age of the 7066 participants was 73.4 years, and 62% were women. The median follow-up duration was 10.0 years during which 791 participants developed dementia (n = 541 Alzheimer’s disease (AD) and n = 155 vascular/mixed dementia (VaD)). The 10-year moving average of PM2.5 concentrations ranged from 14.6 to 31.3 µg/m3.
PM2.5 concentration was positively associated with dementia risk: HR = 1.20, 95% CI (1.08–1.32) for all-cause dementia, 1.20 (1.09–1.32) for AD, and 1.33 (1.05–1.68) for VaD per 5 µg/m3 PM2.5 increase. No association was detected between NO2 or BC exposure and dementia risk.
In this large cohort of older adults, long-term PM2.5 exposure was associated with increased dementia incidence. Reducing PM2.5 emissions might lessen the burden of dementia in aging populations.
Land use regression (LUR) models typically investigate within-urban variability in air pollution. Recent improvements in data quality and availability, including satellite-derived pollutant ...measurements, support fine-scale LUR modeling for larger areas. Here, we describe NO2 and PM10 LUR models for Western Europe (years: 2005–2007) based on >1500 EuroAirnet monitoring sites covering background, industrial, and traffic environments. Predictor variables include land use characteristics, population density, and length of major and minor roads in zones from 0.1 km to 10 km, altitude, and distance to sea. We explore models with and without satellite-based NO2 and PM2.5 as predictor variables, and we compare two available land cover data sets (global; European). Model performance (adjusted R 2) is 0.48–0.58 for NO2 and 0.22–0.50 for PM10. Inclusion of satellite data improved model performance (adjusted R 2) by, on average, 0.05 for NO2 and 0.11 for PM10. Models were applied on a 100 m grid across Western Europe; to support future research, these data sets are publicly available.
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