Objective EXPANSE Urban Labs aims to evaluate uncertainty in exposure assessment, sources of bias, and generalizability of various residential long-term exposure-disease associations reported in large cohort consortia. The objective of this paper is to i) present this large panel study design and methods; ii) describe participants and exposome components in the five centers; and iii) illustrate the study principle in a case study focusing on depression, greenspace, and heat. Methods Data was collected between June 2022 and June 2024 in CH (Basel, Switzerland), ES (Barcelona, Spain), GR (Athens, Greece), NL (the Netherlands), and PL (Lodz, Poland). Participants completed a baseline questionnaire, additional questionnaires repeatedly over 1.5 years, and a two-week measurement campaign consisting of wearing a GPS tracker and a silicone wristband paralleled by a time activity diary; personal and residential PM2.5 monitoring in a subsample; and self-collection of dried capillary blood spots. Results 3782 Participants were on average 48.8 years, 60.9% were females and 49.3% highly educated. Between 16.9% (PL) to 91.0% (CH) completed the measurement campaign. Residential greenness (NDVI within 500 m buffer) was highly correlated with mobility-weighted greenness but not with perceived greenspace quality. Mobility-weighted greenness varied by activity and day of the week. Perceived heat in summer was highest in ES. Ventilator use during night was lowest in CH and NL. A suggestive and exemplary, statistically non-significant association between perceived heat and depression was only evident if perceived greenspace quality is low and no ventilator use is reported. Conclusions This paper sets the stage for future analyses of the Urban Labs data. The Urban Labs data will inform on the priority of additional data to be obtained in future large cohort follow-up questionnaires on urban dwellers’ perception and use of the environment and on adaptation measures.

A persistent disconnect exists between epidemiological studies emphasising long-term health effects and exposure assessments relying on short-term measurements, as well as between health responses linked to outdoor (ambient) concentrations versus translation of that to indoor or personal environments. To fill these gaps, we developed an extrapolation method to estimate annual personal exposure to air pollutants from outdoor sources, including fine particles, nitrogen dioxide, ozone and black carbon, using measurements from personal exposure campaigns. The methodology accounts for (a) hourly variations in background ambient concentrations, (b) magnitude and variation in infiltration efficiency, (c) adjustment for microenvironments, and (d) individual time activity patterns. Longer measurement periods appeared to be associated with greater agreement and lower variability between measurement-period and annually extrapolated estimates of personal exposure to PM2.5 and BC from outdoor sources. We found that ambient measurements assigned by the nearest monitor approach were consistently higher than those estimated by the matched-scaled approach and higher than personal exposure to air pollution from outdoor sources for all pollutants, indicating potential bias in epidemiological associations, under specific conditions concerning variability and random error. Given this discrepancy, caution is warranted when applying outdoor air quality health guideline values to indoor environments.

BACKGROUND:Ambient PM2.5 exposure is strongly associated with adverse health effects, including all-cause mortality. However, the lack of monitoring networks globally necessitates a better understanding of the spatiotemporal distribution of near-surface PM2.5 pollution. While ground-level pollutants are traditionally measured at fixed stations, integrating land use and atmospheric reanalysis data captures the broad geospatial trends and specific aerosol compositions necessary for high-resolution exposure assessment. This study aims to demonstrate the accuracy and reliability of ensemble modeling for estimating near-surface PM concentrations at a high spatiotemporal resolution in Athens, Greece. METHODS:Daily PM2.5 concentrations were estimated using a stacked ensemble machine learning model to incorporate distributed random forest, gradient boosting, and feedforward neural network algorithms to minimize predictive error compared to individual models. The input set for all base learners consisted of daily observations from air quality monitors between 2007-2019 combined with satellite-derived estimates, providing a total of 61 variables describing regional aerosol, weather, and land use characteristics. RESULTS:We observed strong predictive performance in our ensemble model, with a mean R2 of 0.85 and an average error of 4.18 μg/m3. The annual average concentration of PM2.5 (22.30 μg/m3) exceeded current WHO and EU guidelines, with considerable spatiotemporal variation across greater Athens. The highest annual mean PM2.5 concentrations were in 2007 (33.18 μg/m3) and on average year-to-year, PM2.5 concentrations were highest during the mid-winter months, in agreement with the expected seasonal maximum for the region, likely driven by increased residential heating alongside winter meteorological conditions, such as temperature inversions and a shallow, stable planetary boundary layer. SIGNIFICANCE:This is among the first studies to estimate PM2.5 exposures in the greater Athens region at a high spatiotemporal resolution using diverse satellite and land use data. This framework enables the investigation of cumulative exposures, particularly in regions with limited ground-level monitoring.

INTRODUCTION:In air-pollution epidemiology, measured or modelled surrogate exposure estimates, prone to measurement error (ME), are used to investigate the health effects of exposure to pollution of outdoor origin, potentially leading to biased effect estimates. We predicted the annual personal exposure from outdoor sources by using personal measurements, compared it with concentrations from surrogate metrics, and quantified the ME magnitude, type, and determinants. METHODS:We used measurements from four panel studies in London, UK, and predicted personal exposures to fine particulate matter (PM2.5), nitrogen dioxide (NO2), ozone (O3), and black carbon (BC). We compared those with surrogate exposures, including measurements from fixed-site monitors, modelled ambient concentrations, or hybrid methods accounting for people's mobility. We estimated the exposure ME magnitude, correlations, and variance ratios between surrogate measures and personal exposure, and the percentages of classical/Berkson-type errors. Individual- and area-level characteristics, such as age, sex, socio-economic status, and time spent outdoors, were assessed as potential error determinants. RESULTS:Predicted annual personal exposures to PM2.5, NO2, O3, and BC from outdoor sources were overestimated by surrogate metrics, with mean differences of up to 10.1, 40.0, 61.7, and 2.6 μg/m3, respectively. The variance ratios and Pearson correlation coefficients between surrogate and predicted personal exposures ranged from 0.03 to 165.02 and -0.24 to 0.25. Time-activity adjustment reduced errors substantially. Berkson-type errors dominated the ME for PM2.5 and BC (43%-81% and 26%-98%, respectively), whilst classical errors characterized gases (>94% for both NO2 and O3). Time spent outdoors, house type, and deprivation were associated with exposure error. CONCLUSION:The use of surrogate exposures to investigate the health effects of long-term exposure to air pollution from outdoor sources may bias the epidemiological estimates due to ME. Information about the error structures and their determinants can be used for correction and the identification of the true exposure-response functions.

BACKGROUND:Ambient PM10 is associated with mortality; however, potential changes in this association over time and the factors explaining such changes are unclear. Therefore, we aimed to examine whether mortality risk associated with PM10 has changed from 1979 to 2019 and whether changes in socioeconomic or environmental conditions can explain any temporal variation in the association between PM10 and mortality. METHODS:We applied an extended two-stage time-series design to assess temporal change in the association between PM10 and all-cause mortality across 143 cities in 26 countries from 1979 to 2019. In the first stage, city-specific and time-specific associations between PM10 and mortality were estimated using quasi-Poisson regression after each city time series was divided into non-overlapping 3-year segments. In the second stage, these estimates were pooled by use of longitudinal random-effects meta-regression with calendar year as a predictor. We further investigated whether selected socioeconomic and environmental factors explained observed temporal trends by including these variables in the second-stage model. FINDINGS:Totally, 23·2 million deaths were analysed. The overall association between PM10 and mortality had increased from 1979 to 2019, indicating a stronger association at a given PM10 concentration over time. A 10 μg/m3 increase in daily PM10 was associated with a 0·23% increase in all-cause mortality in 1979 (95% CI 0·05-0·41), and this association increased to 0·51% in 2019 (0·36-0·65). Temporal patterns in the PM10-mortality association varied across cities and were positively associated with population ageing over time and negatively associated with annual mean PM10 concentrations. INTERPRETATION:The findings of this study suggest that the effect of a given increment of PM10 on mortality has increased over time. Applying historical risk estimates could underestimate the current health burden. Continuous updating of evidence on the health impacts of air pollution is essential to ensure accurate and valid estimates. FUNDING:Wellcome Trust.

RATIONALE:Long-term exposure to air pollution contributes to chronic respiratory diseases, including asthma and chronic obstructive pulmonary disease (COPD). While the effect of fine particulate matter (PM2.5) and nitrogen dioxide (NO2) are supported by evidence, the contribution of black carbon (bc), a combustion-related pollutant, remains unclear. OBJECTIVES:To investigate associations of long-term exposure to bc as well as PM2.5 and NO2 with incidence of adult-onset asthma and COPD in Denmark. METHODS:We followed 3.2 million Danish residents aged 30 years or older on January 1, 2000 until December 31, 2018, for incidence of asthma and COPD (first hospital contact), and combined incidence (first prescription for obstructive airway disease [OAD] medication). Annual mean concentrations of air pollutants were estimated using European-wide hybrid land-use regression models. Cox proportional hazard models were used with adjustment of demographic, socioeconomic factors, smoking, and body mass index. RESULTS:During 50.7, 50.4, and 44.4 million person-years of follow-up, 52 648 participants developed asthma, 146 269 developed COPD, and 393 211 were prescribed OAD medication, respectively. An interquartile range increase of 2.0 and 10.3 µg/m3, and 0.5 × 10-5/m in PM2.5, NO2, and bc, respectively, were associated with higher risks of asthma incidence (hazard ratio: 1.10 [95% confidence interval: 1.08, 1.13]; 1.16 [1.13, 1.19]; 1.17 [1.14, 1.20]), COPD incidence (1.04 [1.02, 1.05]; 1.05 [1.03, 1.07]; 1.06 [1.04, 1.08]), and OAD medication (1.02, [1.01, 1.03]; 1.05 [1.03, 1.06]; 1.03 [1.02, 1.05]). The observed association with PM2.5 were attenuated or became null after adjusting for NO2 or bc, while those with NO2 or bc remained robust after adjusting for PM2.5. CONCLUSION:In a large Danish nationwide analysis, air pollution is an important predictor for adult-onset asthma and COPD. Our findings suggest that the relevance of pollutants originating from combustion sources, as reflected by the association with bc and NO2, may contribute importantly to these respiratory outcomes. Targeted actions to reduce combustion-related emissions, including those leading to bc formation, may further help decrease the burden of chronic respiratory diseases.

Parkinson’s disease is a progressive neurological condition with significant social burden, expected to rise in the future. Epidemiological research is essential to inform data-driven health policies and unravel new insights into its pathogenesis and potential prevention. We used a drug prescription database with extensive population coverage to calculate, nationwide and regional, incidence and prevalence rates of Parkinson’s disease. Additionally, we calculated mortality rates, crude case fatality risk and mortality rate ratios relative to the general population. The crude incidence, prevalence and mortality rates were estimated at 48 cases per 100,000 person-years [95

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

Previous health impact assessments of temperature-related mortality in Europe indicated that the mortality burden attributable to cold is much larger than for heat. Questions remain as to whether climate change can result in a net decrease in temperature-related mortality. In this study, we estimated how climate change could affect future heat-related and cold-related mortality in 854 European urban areas, under several climate, demographic and adaptation scenarios. We showed that, with no adaptation to heat, the increase in heat-related deaths consistently exceeds any decrease in cold-related deaths across all considered scenarios in Europe. Under the lowest mitigation and adaptation scenario (SSP3-7.0), we estimate a net death burden due to climate change increasing by 49.9

Background: Solar and geomagnetic activity have been linked to a multitude of impacts on human health including cardiovascular disease (CVD), and total non-accidental mortality. However, this has not been assessed in the Eastern Mediterranean Region or the Middle East. Our study aimed to assess the effects of short-term geomagnetic disturbances (GMD) on mortality in six locations across the Eastern Mediterranean and Middle East regions (Athens, Thessaloniki, Crete, Greece; Kuwait City, Kuwait; Limassol and Nicosia, Cyprus). Methods: We used a time series analysis adjusted for temperature and humidity over the period between 1997 and 2019 to estimate the effects of GMD (Kp index, sunspot number - SSN, plasma beta, and interplanetary magnetic field - IMF) on daily total non-accidental, CVD, and respiratory mortality, for each study area. We applied metaanalysis to estimate the pooled GMD mortality effect across all locations. Results: Our analysis included 664,427 deaths over the study period. Kp index was found to be significantly associated with total, CVD, and respiratory mortality. There was a 0.94 % (95 % CI: 0.019, 1.87) increase in total non-accidental mortality; a 0.63 % (95 % CI: 0.013, 1.25) increase in CVD mortality; and a 2.53 % (95 % CI: 0.36, 4.75) increase in respiratory mortality per IQR increase in Kp index (IQR = 15.63). However, solar activity parameters (SSN, Plasma beta, or IMF) were not statistically significantly associated with mortality. Conclusions: Our findings indicate an association between exposure to higher levels of Kp index and total nonaccidental, CVD and respiratory mortality in the Eastern Mediterranean and Middle East Regions. The results warrant additional exploration to ascertain if variations in solar activity-driven human physiological dynamics may also be linked to other health consequences.

Although the short-term heat effects are well-established, longer-term effects, beyond those, have recently received attention, in the context of climate change. Our study aims to investigate the potential effects of long-term exposure to non-optimal warm period temperatures on all-cause mortality in four large regions in the UK, Norway, Italy, and Greece. Daily all-cause mortality counts from 1996 to 2018 for four European NUTS-2 regions including 52-662 small areas were collected and associated with spatiotemporal temperature estimates. A NUTS-2 region-specific mixed quasi-Poisson over-dispersed model, with a random intercept per small area within NUTS-2 regions, was applied to investigate the association between long-term temperature exposure and mortality during the warm period (May to September), adjusting for short-term temperature, seasonality, long-term trends, and small-area population characteristics. As long-term temperature exposure indices per small area, we considered: 1) the warm period annual average temperature, 2) the annual standard deviation (SD) of warm period temperature, and 3) the coefficient of variation of warm period temperature (CV). We found consistent results following short-term temperature exposure on mortality, with higher effects in southern areas. Results on the shape of the long-term association between average temperature and mortality differed by country, while the different temperature metrics produced inconsistent findings. Increased mortality was associated with increased annual warm season temperature, lower SD and increased CV in Greece, with higher SD and decreased CV in Italy and with decreased annual temperature and CV in Norway. Effects in the UK did not reach the nominal level of statistical significance. Although our study implies an impact on mortality resulting from longer-term temperature exposure, its direction varied across areas and on the temperature metric used. Further research is warranted, applying non-ecological study designs and covering various geographical areas to capture the impact of individual and area-specific characteristics.

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

There is an unmet need for large-scale multicenter studies on health effects of long-term exposure to ultrafine particles (UFPs). Such studies have been hampered by the lack of high-resolution models covering large geographical areas. This study aims to develop and evaluate Europe-wide UFP models using mobile measurements. Between 2018 and 2024, we conducted UFP mobile measurement campaigns in nine European areas. We developed both Europe-wide pooled and area-specific models of UFP concentrations at a 25 m spatial scale, using supervised linear regression with a deconvolution approach. The performance of our pooled models, with and without deconvolution, was similar, exhibiting an overall R2 of 0.33-0.36, 0.34-0.35 in 5-fold cross-validation (CV), and 0.29 in leave-one-area-out CV. When evaluated against external independent, longer term off-road measurements collected from seven areas and countries, our deconvoluted model effectively captured UFP variability in both urban and rural settings, with R2 ranging from 0.25 to 0.70, modestly better than the non-deconvoluted model (R2 = 0.23-0.67) and superior to area-specific models (R2 = 0.15-0.42). These findings underscore the ability of our pooled deconvoluted Europe-wide UFP model to capture the variability of UFP across diverse environments. The UFP model estimates will facilitate large-scale multicenter studies to investigate the long-term health effects of UFP exposure.

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

The regional deposited dose of ultrafine particles in the respiratory tract and their transport to the olfactory region was investigated through an existing particle dosimetry model (Exposure Dose Model 2, ExDoM2). The original dosimetry model was adapted to include a methodology that uses numerical modelling for the transport of ultrafine particles from the nose to the olfactory region. The mass dose to the oesophagus, blood, and lymph nodes was also calculated. Four different cases were studied: heating, traffic, nucleation events and background levels. The results showed that deposition in the olfactory region decreased with increasing particle size (from 0.40 % to 0.12 %). The majority of particles were estimated to penetrate into the thoracic region with 36 % of particles within the size range 14-33 nm deposited in the alveolar-interstitial region, followed by the tracheobronchial (21 %), the extrathoracic (11 %) and olfactory (<0.5 %) regions. In addition, a comparison between the mass, surface, and number doses indicated different governing sources such as a higher number dose was obtained during nucleation (10.5 x 10(8) particles), while higher mass (9.4 x 10(-2) mu g) and surface (7.1 x 10(12) nm(2)) dose was obtained during heating periods. Simulations also indicated that after clearance, 56.9 % of ultrafine particles were found in the alveolar region, a finding that is linked to their small size and low clearance rate of this region. Nevertheless, the dose per unit surface area and the dose per cell in the olfactory region were higher than in the alveolar-interstitial region.

Supplementary Figure S3 shows the results from single- and multi-pollutant models and the cumulative risk index for breast cancer

Epidemiological cohort studies associating long-term exposure to ambient air pollution with health outcomes most often do not account for individually assigned exposure measurement error. Here, we implemented Cox proportional hazards models to explore the relationships between NO2, PM2.5 and ozone exposures with the incidence of natural-cause mortality and several morbidity outcomes in 61,797 London-dwelling respondents of the UK Biobank cohort. Data from an existing personal monitoring campaign was used as an external validation dataset to estimate measurement error structures between "true" personal exposure and several surrogate (measured and modelled) estimates of assigned exposure, allowing for the application of two health effect estimate correction methodologies: regression calibration (RCAL) and simulation extrapolation (SIMEX). Uncorrected hazard ratios (HRs) suggested an increase in the risk of natural-cause mortality for modelled NO2 estimates (HR: 1.028 [0.983, 1.074] per IQR increment of 14.54 μg/m3) and no statistically significant association was observed for PM2.5 surrogate exposure measures. Measurement error corrected HRs were generally larger in magnitude, although exhibited wider confidence intervals than uncorrected effect estimates. Chronic obstructive pulmonary disease (COPD) was associated with increased exposure to modelled NO2 (1.087 [1.022, 1.155]). Both RCAL and SIMEX correction resulted in increased HRs (1.254 [1.061, 1.482] and 1.192 [1.093, 1.301], respectively). SIMEX correction of modelled PM2.5 (IQR: 1.72 μg/m3) associations with COPD increased the HR (1.079 [1.001, 1.164]) in comparison to uncorrected (1.042 [0.988, 1.099]). These findings suggest that health effect estimates not corrected for exposure measurement error may lead to underestimation in the magnitude of effects.

Epidemiological studies have documented the health effects of long-term exposure to fine particulate matter, while there is a growing number of studies looking into associations with one of its main components elemental carbon (EC) and its related metrics such as black carbon (BC), black smoke (BS) or aerosol light absorption coefficient often referred as “PM absorbance”. We performed a systematic review and meta-analysis on the associations between long-term exposure to elemental carbon (EC) and disease incidence. We searched for studies published up to April 2025, assessing long-term to EC-related exposure (also including BC, BS, PM absorbance) and incidence of ischemic heart disease (IHD), asthma, chronic obstructive pulmonary disease (COPD) and lung cancer in adults, and asthma and acute lower respiratory infections (ALRI) in children. We pooled effect estimates by random-effects models and investigated heterogeneity by region and risk of bias assessments. The certainty of the evidence was assessed using the Grading of Recommendations Assessment Development approach. We included 51 studies assessing long-term exposure to EC and disease incidence. The pooled relative risk (RR) for a 1 µg/m3 increase in EC was 1.10 (95

Supplementary Figure S5 shows the results for air pollutants and breast cancer of models including confounders violating the assumption of proportional hazards as strata