In Europe, lack of consistent noise data has hampered large-scale epidemiological studies and disease burden assessments related to noise in Europe. This study addressed these limitations by developing and evaluating a Europe-wide noise model using CNOSSOS-EU, a standardized noise calculation framework in Europe.We implemented the CNOSSOS-EU model using harmonized input data, including traffic flow estimates for all roads. We compared our noise estimates with national estimates from three countries and one city in a agreement analysis. As a proof of concept, we estimated noise levels at the noisiest façade points of 102,560 randomly-selected buildings across Europe and 14 million buildings in the cohort study areas.Overall, plausible day-evening-night levels (Lden) were modelled at points with varying traffic flow on nearby roads. The Lden model estimates showed moderate correlations with national noise models in Switzerland, the Netherlands, and the United Kingdom (Pearson's cor = 0.52-0.77) but a lower correlation in Stockholm County (0.39). Most randomly-selected building façade points (62%) had traffic flows on nearby roads below the END modeling threshold, with substantial noise variability. Because the input traffic flow estimates on residential roads explained little variability, noise estimates should be applied with caution where residential roads dominate the noise exposure.While high-quality national models remain gold-standard and preferable for health analysis and impact assessments where available, our Europe-wide model offers standardized estimates across countries and expands beyond END maps. Both Europe-wide and national estimates should be considered in sensitivity analyses to assess potential differences in estimated health effects. The resulting noise estimates may further facilitate our understanding of noise-related health effects and disease burden at a broader scale in Europe for countries lacking national noise models.

Although people spend most of their night-time hours indoors, environmental noise exposure is typically assessed using outdoor levels. This study examined outdoor-to-indoor noise attenuation across 49 dwellings in Greater London using synchronized and unsupervised measurements, while noise levels were expressed via the A-weighted equivalent (), maximum (), and percentile (, , ) noise indicators. Moderate-to-strong correlations between night-time outdoor levels and outdoor-to-indoor attenuation levels (-) informed the development of multiple and mixed-effect linear regression models to estimate indoor noise levels based on outdoor levels. Mixed-effect models outperformed multiple linear models (RMSE: 0.7-4.9 vs 2.3-5.7 dB(A)), with outdoor levels accounting for most variability, and with additional contributions from the occupation status, window size, and room volume predictors. The estimated attenuation levels ranged from 20 to 26 dB(A), with in line with the WHO recommended 25 dB(A) level. The proposed modeling approach enables estimates of indoor noise exposure, offering a more representative basis for night-time exposure-response assessments in the UK and similar urban settings.

Airborne particulate matter (PM) is a complex mixture of particles thought to be associated with a range of adverse health effects, including female breast cancer. Current evidence on the association between PM and female breast cancer risk is inconsistent. This study investigated the association between long-term exposure to PM and breast cancer risk in a nested case-control study within the French E3N-Generation cohort including 5222 breast cancer cases identified over the 1990–2011 follow-up period and 5222 individually matched controls. Annual mean concentrations of PM10 and PM2.5 at participants’ residential addresses, were estimated using a land use regression model. Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated using conditional logistic regression models. ORs for each 10 µg/m3 increase in the average of PM2.5 and PM10 were 1.14 (95% CI: 0.99–1.30) and 1.08 (95% CI: 0.98–1.18), respectively. When restricted to invasive ductal and lobular carcinomas, ORs were 2.74 (95% CI: 1.05–7.15) for PM2.5 and 2.05 (95% CI: 1.11–3.78) for PM10. Comparable effects of PM exposure estimated by a chemistry transport model reinforces these findings. This study suggests a potential association between PM2.5 and PM10 exposure and breast cancer risk.

A major shortcoming of air pollution and road traffic noise models is a lack of traffic data, especially for local roads. We aimed to increase the coverage and accuracy of data on annual average daily traffic (AADT) flows on all road types to support the development of emission inventories. We developed statistical machine learning approaches (Random Forest - RF; extreme gradient boosting - XGB; generalised linear mixed model - GLMM) to model AADT, using open-data on measured traffic flows from 4744 sites, covering 12 areas in 7 European Countries. Traffic flow predictor variables (N = 51) described the types of roads, road network connectivity, population, drive-time, land use and topography. A 1000-fold cross-validation procedure was used in model training and testing, where a randomly stratified (by road type and area) selection of 80% of traffic counting sites were used to train models, with the remaining 20% of sites reserved for model testing. The predictions for all reserved sites from the 1000 iterations were pooled to provide an 'ensemble' prediction for each site. For performance evaluation, we compared 'single' models developed using the whole dataset with models 'combined' from road-type sub-models (highways, local, residential). RF and XGB performed similarly overall (R2: 0.85-0.86) and outperformed GLMM (R2: 0.69-0.73). The 'combined' method slightly outperformed 'single' models overall, with notably better performance for residential roads by using sub-models. For transferability to other areas, we recommend testing the need for local calibration of the models (i.e., regression) using measurements of AADT covering different road types.

Transportation noise affects millions of people globally. This umbrella review assesses the quality of the evidence across existing systematic reviews on the association between environmental transportation noise (from roads, railways and aircraft) and child and adolescent mental health outcomes (depression, anxiety, hyperactivity/inattention, behavioural problems, and emotional symptoms). It also identifies inequality in associations for factors such as deprivation and ethnicity.Seven databases were searched from January 2015 up to January 2025. Studies were screened for inclusion independently by two reviewers. The AMSTAR2 (A MeaSurement Tool to Assess systematic Reviews 2) framework was used to critically appraise the systematic reviews.A total of eight systematic reviews and one report from grey literature were included. Each noise source (road, railway and aircraft) showed a mixture of associations with mental health outcomes. For example, reviewing the evidence available, existing systematic reviews suggest that road traffic noise has a harmful association with anxiety but no association with depression.The overall quality of the evidence varied widely, ranging from very low to moderate. Significant evidence gaps were identified on railway noise as an exposure source and the association of noise with anxiety and depression. A notable gap was the absence of meaningful subgroup analyses by socioeconomic deprivation or ethnicity, limiting understanding of potential effect inequalities and vulnerability to environmental noise exposure.The findings indicate a heterogenous evidence base, underscoring the need for more robust, longitudinal investigations using standardised exposure and outcome assessments to increase certainty of the estimates. Future research should focus on the associations of transportation noise with depression and anxiety; and should incorporate an inequality perspective to better understand the distribution of these associations and address population health disparities.

BACKGROUND:Associations have been found between early-life air pollution exposures and lung function during childhood, but evidence is limited on lung function growth. OBJECTIVES:Evaluate whether elevated air pollution during pregnancy and early life is associated with reduced lung function growth aged 8-24 years. METHODS:We investigated ∼5200 children from the ALSPAC UK birth cohort with longitudinal lung function data at ages 8, 15 and 24 years. Mixed-effects linear models with individual level random intercepts were used to evaluate the associations between prenatal and early life exposure to source-specific PM10 (total/road/other) and NO2 and annual growth in forced expiratory volume (FEV1), forced vital capacity (FVC), forced expiratory flow (FEF25-75) and FEV1/FVC lung function z-scores, adjusting for preterm birth, SES, smoke exposure/current smoking, damp, season, allergy, breastfeeding and BMI. RESULTS:We found small reductions in all measures of annual lung function growth z-scores across ages 8-24 years, in relation to prenatal and early-life air pollution exposure, with -0.0122 (-0.016, -0.008) FEV1(z) per 1 unit increase in PM10_road exposure. The magnitude of effect was larger for lung function growth from age 8 up to age 15 years, -0.0396 (-0.046, -0.033) FEV1(z), than to age 24 years. CONCLUSION:Exposure to higher levels of source-specific PM10, and NO2 air pollution, during pregnancy and early childhood may impact lung function development, with greatest effects in adolescence, the time of fastest growth. Results are consistent with earlier findings within other cohorts with follow-ups into adolescence, but extends findings to age 24 years and mid-expiratory flows.

Supplementary Table S1 shows Spearman correlations per (sub) cohort between NO2, PM2.5, BC, and O3 (warm season) among participants with full information in the main model

Data on road traffic speeds is needed for air and environmental noise pollution modelling, for regulatory control, exposure assessment and assessing health impacts. However, vehicle speed data is often not available and national speed limits, by road classification, may be used as a proxy instead. This may contribute to uncertainties in model predictions. This study presents novel methods, applied to satellite imagery, to calculate vehicle speed road by road for entire cities, demonstrating their use in Barcelona. The approach exploits the fraction of a second gap between eight multi-spectral sensors onboard the WorldView-2 and-3 satellites and was used to estimated speeds for 128,206 vehicles on motorways, trunk, primary, secondary and tertiary roads from 10 satellite images. The average estimated vehicle speeds ranged from 69 km/h on motorways to 31 km/h on tertiary roads. Satellite-derived vehicle speeds showed good agreement with Directions API speeds on a subset of roads (R2 = 0.71, NMGE = 0.23 and RMSE 13.4 km/h). A Normalised Mean Bias of 0.04 suggests that on average the satellite and Directions API estimates were similar. Using Directions API as the benchmark, satellite-derived estimated speeds yielded values of RMSE of 13.4 km/h, compared with and RMSE of 25.7 km/h using road type national speed limits, for a subset of roads. In this pilot study, therefore, satellite-derived vehicle speeds yield a 48% reduction in error over using the national speed limit by road type. This paper shows that high- resolution satellite imagery has potential to quantify vehicle speed in cities.

Supplementary Figure S1 shows box plots of exposures by individual (sub-) cohort study.

BACKGROUND:Existing evidence on associations between exposure to air pollution and psychological distress from middle to older age is limited by consideration of short exposure periods, poor historical covariates, exposures and outcomes, and cross-sectional study designs. We aimed to examine this association over a 26-year period between ages 43 and 69. METHODS:We utilised data from the Medical Research Council National Survey of Health and Development Study (the 1946 British birth cohort). Land-use regression models estimated exposure to specific air pollutants using household addresses for 1991 (NO2), 2001 (PM10, NO2), and 2010 (NO2, NOx, PM10, PM2.5, PMcoarse, PM2.5abs). These were linked to the closest data collection wave at ages 43, 53 and 60-64, respectively. Psychological distress was assessed through the 28-item version of the General Health Questionnaire (GHQ-28), at ages 53, 60-64 and 69. Associations between each of the pollutants with psychological distress were analysed using generalised linear mixed models, adjusted for pollution exposure before age 43, assigned sex, social class, smoking status, neighbourhood deprivation, and previous mental health problems. We also examined effect modification by social class. RESULTS:At age 69, 2125 participants completed the GHQ-28. In fully adjusted models, higher NO2 exposure was associated with higher GHQ-28 scores across a 26-year period (β=0.023, 95%CI:0.005, 0.040 per interquartile range increase in exposure), whereas higher exposure to PM10 was associated with lower GHQ-28 scores across a 16-year period (β=-0.021, 95%CI:-0.037, -0.006). There was no evidence of associations between exposure to other pollutants at age 60-64 and GHQ-28 at age 69. We found no effect modification by social class. CONCLUSIONS:In this cohort there was some evidence of an association between higher cumulative exposure to NO2 and higher psychological distress, but mixed associations with other exposures. Policies to reduce pollutant exposure may help improve psychological symptoms in middle to late adulthood.

Background Aircraft noise is a growing concern for communities living near airports. Objectives This study aimed to explore the impact of aircraft noise on heart structure and function. Methods Nighttime aircraft noise levels (Lnight) and weighted 24-hour day-evening-night aircraft noise levels (Lden) were provided by the UK Civil Aviation Authority for 2011. Health data came from UK Biobank (UKB) participants living near 4 UK major airports (London Heathrow, London Gatwick, Manchester, and Birmingham) who had cardiovascular magnetic resonance (CMR) imaging starting from 2014 and self-reported no hearing difficulties. Generalized linear models investigated the associations between aircraft noise exposure and CMR metrics (derived using a validated convolutional neural network to ensure consistent image segmentations), after adjustment for demographic, socioeconomic, lifestyle, and environmental confounders. Mediation by cardiovascular risk factors was also explored. Downstream associations between CMR metrics and major adverse cardiac events (MACE) were tested in a separate prospective UKB subcohort (n = 21,360), to understand the potential clinical impact of any noise-associated heart remodeling. Results Of the 3,635 UKB participants included, 3% experienced higher Lnight (≥45 dB) and 8% higher Lden (≥50 dB). Participants exposed to higher Lnight had 7% (95% CI: 4%-10%) greater left ventricular (LV) mass and 4% (95% CI: 2%-5%) thicker LV walls with a normal septal-to-lateral wall thickness ratio. This concentric LV remodeling is relevant because a 7% greater LV mass associates with a 32% greater risk of MACE. They also had worse LV myocardial dynamics (eg, an 8% [95% CI: 4%-12%] lower global circumferential strain which associates with a 27% higher risk of MACE). Overall, a hypothetical individual experiencing the typical CMR abnormalities associated with a higher Lnight exposure may have a 4 times higher risk of MACE. Findings were clearest for Lnight but were broadly similar in analyses using Lden. Body mass index and hypertension appeared to mediate 10% to 50% of the observed associations. Participants who did not move home during follow-up and were continuously exposed to higher aircraft noise levels had the worst CMR phenotype. Conclusions Higher aircraft noise exposure associates with adverse LV remodeling, potentially due to noise increasing the risk of obesity and hypertension. Findings are consistent with the existing literature on aircraft noise and cardiovascular disease, and need to be considered by policymakers and the aviation industry.

Exposure to road traffic noise in residential settings has been associated with detrimental effects on health including annoyance, sleep disturbance, cardiometabolic outcomes, and mental health. Conversely, exposure to natural sounds improves cognitive performance and aids in stress recovery in humans. Environmental exposure studies have shown that the distribution of noise exposures is often not equitable across cities, but evidence related to noise in UK greenspaces remains limited. This study provides an analysis of noise variability and inequalities in noise levels for greenspaces in Greater London related to residential addresses. Noise levels from major and minor roads were modelled across 2,532 greenspaces for the daytime and evening period in accordance with the European Commission Common Framework for Noise Assessment (CNOSSOS-EU) methods from Environment Noise Directive 2002/49/EC and the inverse square law of sound attenuation. Using modelled road traffic noise estimates, we found that 28 % of greenspaces exceeded equivalent World Health Organization noise guideline levels during daytime and evening periods. Greenspaces in Central London were more likely to have noise levels that exceeded the WHO noise guidelines. Distance-based proximity analyses showed that for populations in Central London, greenspace areas nearest to residential addresses were more likely to feature high noise levels. As distance travelled from residential locations increased, the distribution of high greenspace noise levels became more dispersed. However, no inequality gradient was observed between different deprivation groups, except for the least deprived communities’, who experienced noise levels that were 2 dB lower within greenspace areas within a 5 km radius.

BACKGROUND:Previous research has linked higher exposure to air pollution to increased cognitive impairment at older ages. We aimed to extend the existing evidence in this area by incorporating exposures across the life course in addition to measures of cognition and brain structural imaging in participants at midlife to older age. METHODS:For this population-based study, we used data from the Medical Research Council National Survey of Health and Development (NSHD; also known as the 1946 British Birth Cohort) and a neuroimaging substudy of the NSHD known as Insight 46. Participants were recruited after birth in a single week during March, 1946. Our objectives were to assess whether exposure to air pollutants in midlife (age 45-64 years) was associated with poorer processing speed and poorer verbal memory between the ages of 43 years and 69 years, and whether exposures were associated with poorer cognitive state and brain structure outcomes at age 69-71 years. Air pollution exposure data were available for nitrogen dioxide (NO2; ages 45-64 years); particulate matter with diameter less than 10 μm (PM10; ages 55-64 years); and nitrogen oxides (NOx) and particulate matter with diameters less than 2·5 μm (PM2·5) and between 2·5 μm and less than 10 μm (PMcoarse) and particulate matter absorbance (PM2·5abs) as a measure of black carbon absorption (ages 60-64 years), with adjustments for early-life exposures to black smoke and sulphur dioxide. Verbal memory was tested with a 15-item recall task and processing speed with a visual search task at ages 43, 53, 60-64, and 69 years. The Addenbrooke's Cognitive Examination III (ACE-III), a measure of cognitive state, was conducted at age 69 years. Whole-brain, ventricular, hippocampal, and white matter hyperintensity volumes were assessed by MRI at age 69-71 years. Generalised linear models and generalised mixed linear models were used to explore associations between pollution exposure, cognitive measures, and brain structural outcomes, adjusted for sociodemographic factors including smoking status and neighbourhood deprivation. FINDINGS:Between the ages of 43 years and 69 years, we included 1534 NSHD participants in the verbal memory and processing speed analysis. Of 2148 participants who underwent testing during the wave of follow-up in 2015-16, at age 69 years, 1761 were included in the ACE-III analysis. Of the 502 NSHD participants recruited into the Insight 46 substudy, 453 were included in the analysis. Higher exposure to NO2 and PM10 was associated with slower processing speed between the ages of 43 years and 69 years (NO2 β -8·121 [95% CI -10·338 to -5·905 per IQR increase in exposure]; PM10 β -4·518 [-6·680 to -2·357]). Higher exposure to all tested pollutants was associated with lower ACE-III score at age 69 years (eg, NO2 β -0·589 [-0·921 to -0·257]). Higher exposure to NOx was associated with smaller hippocampal volume (β -0·088 [-0·172 to -0·004]) and higher exposure to NO2 and PM10 was associated with larger ventricular volume (NO2 β 2·259 [0·457 to 4·061]; PM10 β 1·841 [0·013 to 3·669]) at age 69-71 years. INTERPRETATION:Acknowledging the probable effects of exposure early in life, higher exposure to nitrogen dioxide, nitrogen oxides, and coarse particulate matter in midlife to older age was associated with poorer cognition, processing speed, and brain structural outcomes, strengthening evidence for the adverse effects of air pollution on brain function in older age. FUNDING:The National Institute for Health and Care Research, the Medical Research Council (MRC), Alzheimer's Research UK, the Alzheimer's Association, MRC Dementias Platform UK, and Brain Research UK.

Supplementary Figure S4 shows the natural cubic splines for air pollutants and breast cancer incidence