BACKGROUND:Long-term exposure to air pollution has been linked to Parkinson's disease (PD) incidence, yet evidence is mixed, partly because of challenges with PD diagnosis and definition. We examined this association in a nationwide administrative cohort. METHODS:We followed 3,280,190 Danish residents ≥30 years old from January 1, 2000 until December 31, 2018 for PD incidence, defined as either first hospital contact for primary PD or redeemed prescription of PD medication, as recorded in the Danish National Patient Registry or Prescription Registry, respectively. We assigned annual mean air pollution exposure concentrations at baseline residential address using the hybrid land-use regression model (fine particulate matter [PM2.5], nitrogen dioxide [NO2], ozone [warm-season, O3w], black carbon [BC]) rendered at 0.1 × 0.1 km. We used Cox proportional hazard models adjusting for age, sex, individual-level, and area-level socioeconomic factors. RESULTS:During a mean (standard deviation) follow-up of 15.7 (5.6) years, 36,665 participants developed PD. Median (interquartile range [IQR]) exposure levels of PM2.5, NO2, O3w, and BC were 12.4 (2.0), 20.2 (7.9), 80.2 (4.3) μg/m3, and 1.01 (0.4) × 10-5/m, respectively. Hazard ratios (95% confidence intervals) for associations between air pollutants (per IQR) and PD incidence were: 1.05 (1.03, 1.07) for PM2.5; 1.03 (1.01, 1.05) for NO2; 0.98 (0.97, 1.00) for O3w; and 1.04 (1.02, 1.06) for BC. CONCLUSIONS:In a representative nationwide cohort, we find that long-term exposure to air pollution is associated with PD incidence. This unique study, with access to incidence data from administrative health registers, provides new evidence supporting air pollution as a PD risk factor. © 2026 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

The light absorbance of PM-samples on PTFE filters is often used as a measure for exposure to diesel soot in large-scale health studies. Absorbance is a synonym for the optical parameter “absorption coefficient” (AbsC). The formal exposure measure for diesel soot is the mass concentration of the light absorbing species of PM, viz. elemental carbon (EC). In the central health effects study by Janssen et al. [2011] a relation between AbsC and EC is presented, however with an overall uncertainty of 2.5. In the present study, we started with an analysis of the measuring approach of light absorption according to ISO-9835 [ISO, 1993]. Following this procedure, absorption is probed in reflection and expressed in the ratio of the intensity of light reflected from/by a clean and a loaded filter. The AbsC is the logarithm of this ratio (which is known as optical depth) scaled to the volume of air sampled and loaded filter area. We first critically reanalysed the studies used by Janssen et al. [2011] in which the equivalency factor between absorbance and EC concentration was given. We found a good linear relationship when we selected only those data points for which the optical density (OD) was within the proper limits of 0.05 and 2.0. We then analysed which methods had been used to obtain EC data in those studies (and also more recent ones) and selected only those studies where EC had been determined with an official reference approach, i.e. the USreference method NIOSH-5400. The overall relation of EC mass concentration in µg m−3 and AbsC in units of 10–5 m−1 was 0.8 (R2 = 0.92), or 1.0 according to the EU-reference method EUSAAR2-TOT with a factor of 1.25 between US and EU reference EC values. This highly improved estimate of equivalence factors between AbsC and EC might be used to translate the results of existing health effects studies (based on AbsC) to studies using current EC monitoring data (as prescribed in EU-guidelines for air quality) to investigate the possible health effects at a given EC level.

While air pollution is an established risk factor for ischemic stroke (IS), evidence on how socioeconomic position (SEP) modifies this relationship remains limited and inconsistent. We investigated the interplay between air pollution exposure, socioeconomic factors, and IS risk. In this population-based cohort study of 11,919,257 adults (≥18 years), we assessed residential exposure to PM2.5, PM10, and NO2 from 2014 to 2019 in the Netherlands. Using time-varying Cox proportional hazards models, we examined both hazard ratios (HRs) and absolute risk differences (ARDs) for IS incidence across both individual and neighborhood-level SEP factors, including wealth, education, employment history, and a composite SEP score. In total, we observed 151,777 IS cases during the 6-year follow-up period. Higher air pollution exposure was associated with increased IS risk (HRs per standardized increment: 1.19 [95% CI: 1.17; 1.22] per 5.0 µg/m³ higher PM2.5, 1.14 [1.11; 1.16] per 5.0 µg/m³ higher PM10, and 1.09 [1.07; 1.10] per 10 µg/m³ higher NO2. These HRs translated to absolute risk differences in the cumulative 6-year risk. Scaled to populations of 100,000 individuals, the same contrasts in exposure were associated with an expected 1,735 IS cases [95% CI: 1,623; 1,825] for PM2.5, 1,256 cases [1,169; 1,328] for PM10, and 798 cases [755; 833] for NO2 over the six-year period. The association was strongest in middle-SEP groups, particularly for PM2.5, while the highest SEP quintile showed attenuated risks. Area-level analyses revealed stronger associations in lower SEP areas, with variations across wealth, education, and employment components. The impact of air pollution exposure on IS risk varies significantly across socioeconomic groups, with middle-SEP individuals showing particular vulnerability. These findings suggest the need for targeted interventions in susceptible populations and highlight the importance of considering both individual and area-level socioeconomic factors in stroke prevention strategies.

Micro- and nanoplastics (MNPs) are abundant in the environment, with traffic-related tyre-wear contributing substantially to atmospheric levels. MNPs have been detected in human tissues, however, their health effects remain poorly known. Therefore, we assessed immunological and respiratory health changes associated with short-term exposure to traffic-related MNPs in healthy, young adults. In a semi-controlled study design, 23 healthy subjects participated in four-hour exposure sessions at three different sites: a highway, a stop-and-go high-traffic location and an urban park. Directly before and after, and the following morning, we collected venous blood samples to assess changes in total and differential white blood cell (WBC) counts. Before and directly after each visit we measured lung function and respiratory symptoms. During each exposure session, atmospheric synthetic and natural rubber particles were collected and measured using pyrolysis gas-chromatography coupled to mass-spectrometry, within particles smaller than 10 µm (PM10). Additionally, traffic-related combustion and brake wear-related pollutants were measured including PM10, ultrafine particles, black carbon, trace metals and polycyclic aromatic hydrocarbons. Mixed model analyses were used to assess changes in WBC counts and lung function from baseline to post-exposure (pairwise differences), adjusting for time-varying and subject-dependent covariates. We observed significant associations between an interquartile range increase in multiple tyre-wear rubber markers and a 7.1-9.3 % elevation in monocytes in blood obtained immediately after exposure. Moreover, in blood obtained the following morning, these associations not only persisted but increased for monocytes (9.3-17.7 %) and were additionally found for neutrophils (7.4-14.0 %). The associations remained consistent after adjusting for other traffic-related air pollutants. Null associations were found with lung function and symptoms. Short-term exposure to traffic-related MNPs was associated with an increase in total and differential WBCs. Associations might be indicative of pro-inflammatory effects in healthy people, which could lead to clinically relevant responses in vulnerable populations.

We evaluate systematic differences between weather station-based temperature exposures and exposures derived from a number of different spatially resolved temperature databases in the context of short and long-term epidemiological research. We compared daily ambient temperature data across multiple European cities from the following four sources: i) weather station networks (Ta_WS); ii) land surface temperature (LST); iii) ERA5-land (Ta_ERA5); and iv) statistical models (Ta_EXP). We calculated the spatial and temporal variability for each of the four temperature datasets and their pairwise agreement using correlation coefficients, mean bias error (MBE) and root mean squared error (RMSE). We found very high temporal agreement between all pairs of temperature datasets. In contrast, spatial correlations were only high for LST and Ta_EXP (r: 0.89, other pairs r < 0.4). LST and Ta_EXP showed higher spatial variability linked to urban topography when compared to Ta_ERA5 and Ta_WS. During extreme heat days, Ta_EXP and LST showed average spatial temperature variability above 2C° and 4C°. However, LST temperature variability and pairwise agreement against ambient temperature datasets showed seasonal differences with LST overestimating temperatures and thermal contrasts in summer and underestimating Ta during winter. For citywide time-series studies product choice has a limited effect on epidemiological research as all tested products showed similar daily trends. For studies focusing on individual or small-area levels, higher resolution products are required to capture spatial temperature contrasts. Statistical models show a good balance between using LST as predictor to tap its abundant spatial information and limiting LST season-specific over- and underestimation of temperature and temperature contrasts by calibrating predictors with weather stations data.

Ammonia (NH3) and primary PM10 emitted by livestock production affect health and biodiversity, making their reduction essential. Quantities of emitted NH3 and PM10 vary across different livestock species, potentially leading to different regional spatial patterns of NH3 and PM10. This complicates the development of effective mitigation strategies. This study aims to provide insight into how different livestock production animals affect spatial patterns of NH3 and PM10. The study area of similar to 40 x 50 km2 encompassed a livestock-dense area with similar to 2000 farms, several residential clusters and nature parks in the Netherlands. Spatial concentration patterns were predicted for similar to 100,000 receptor points on a 100 x 100 m(2) grid using a dispersion model based on farm emissions. Model assumptions were evaluated through sensitivity analyses. Livestock production emissions significantly increased local levels of NH3 and more moderately elevated local levels of PM10. Spatial concentration patterns were strongly driven by geospatial distributions of farms as well as livestock species, with elevated concentrations observed in areas where farms were densely clustered. The distribution of farm contributions to total NH3 concentrations at receptor points was characterized by numerous small contributions from multiple farms across the study area. Concentrations were higher in rural parts of the study area and characterized by the combination of these small contributions with a few large contributions from nearby farms. Inclusion of farms in a wide radius was especially important for modelling NH3 concentrations in nature areas. These findings imply that generic reduction of livestock farm emissions should be investigated for the formulation of mitigation strategies.

One predominant source of microplastics emitted into the atmosphere is tyre-and road wear particles (TRWPs). Only a handful of studies have quantified atmospheric TRWP concentrations. Our objective was to study variations in TRWPs, compared to other primary traffic pollutants, at locations with different traffic conditions. In 2022-2023, three locations with different traffic-flow and speed (a stop-and-go busy road, highway and urban park), were repeatedly visited for 4-hrs resulting in 23 measurement days. Particles were collected on quartz filters using a high-volume sampler with PM10-inlet and analyzed using double-shot pyrolysis-gas chromatography-mass spectrometry for the mass of synthetic-and natural rubbers (NR). Concentrations of combustion and brake-wear-related traffic air pollutants were measured, including PM10, black carbon (BC), ultrafine particles (UFP) and trace elements. We calculated spatial contrasts and correlations. We observed relatively low levels of sampling-and analysis rubber marker contamination. Synthetic-and NR levels ranged between 2.9 to 42.5 ng/m3 and 1.6 to 26.8 ng/m3, respectively. Compared to the park, rubber markers were 2.8 to 4.6 times higher at the stop-and-go and 2.0 to 2.7 times higher at the highway. These contrasts were larger than for UFP and PM10, but similar to BC and brake-wear related components. Park synthetic rubber levels were modestly higher than field blanks. Rubber markers were highly correlated (r = 0.66 to 0.98) and weakly correlated with most other air pollutants, except for BC and brake wear-related trace elements (r = 0.36 to 0.77). We found substantially increased atmospheric TRWP levels near major roads compared to a park. The measurements from this study will be used for testing associations with health effects.

In this article, we summarise recent developments, identify gaps, and propose a research agenda for quantitative health impact assessment (HIA) of environmental exposures linked to urban transport and land use. This is based on a workshop of 30 experts, complemented by targeted literature identified by participants to illustrate the state of research and practice gaps. The practice of quantitative HIA in urban transport and land use interventions covers a diverse range of methods, models, and frameworks. The selection of an appropriate model depends upon the use case, i.e., the research question, resources and expertise, and application. The plurality of models can be a strength if differences are explicit and their implications are understood. A major gap in most assessments and frameworks is the lack of equity consideration. This should be integrated into all stages of the HIA, considering exposures, susceptibility, disease burden, capacity to benefit, household budgets, responsibility for harm, and participation in the process. Scenarios of environmental exposures in urban transport and land use interventions are often overly simple, while the scenario design process of spatial planning is often opaque. Researchers should specify the involvement of stakeholders and the data, evidence, or behavioural model used to construct the scenario. Recent developments in exposure assessment (remote sensing and modelling) have increased the capacity to conduct HIAs for small geographies at scale. At the same time, advances in simulation have enabled the representation of behaviours at high spatial and temporal resolution. The combination can enable person-centric measures accounting for location, activities, and behaviours, with HIA proceeding ahead of epidemiology. Most HIAs still use Comparative Risk Assessment. This is suitable for estimating the disease burdens of environmental exposures, but more advanced longitudinal methods are better suited for studying interventions. Beyond health outcomes, well-being must be incorporated. The monetisation of health outcomes through welfare economics remains contentious. Representation of uncertainty is increasingly acknowledged. Value of Information methods can inform where new data collection would most efficiently reduce final result uncertainty. In the context of the climate crisis and related environmental limits, methods are needed that consider adaptation alongside mitigation and prevention and test robustness to an increasingly unstable future.

Ultrafine particles (UFP), commonly expressed as particle number concentrations (PNC), have been associated with harm to human health yet are currently not regulated or routinely monitored in many places. This has limited the potential for studies of health effects of long-term exposure to UFP. The present study aims to understand the spatial and temporal variation in fa & ccedil;ade-level UFP exposures in Copenhagen, Denmark. We measured PNC at the fa & ccedil;ades of 27 residences across the city for approximately 72 h each in two campaigns and continuously at an urban background reference site for twelve consecutive months, using portable monitors (miniature diffusion size classifiers [DiSCminis]). We estimated annual means at the residential sites based on temporal adjustment using reference site data. Furthermore, we co-located the DiSCminis at a regulatory monitoring station on three occasions and compared daily means from our reference site to those from seven fixed-site monitoring stations throughout the city. Annual mean PNC at the reference site was 4715 (SD of hourly mean: 3001) pt/cm3, while annual means at 27 residences were slightly higher with a mean of 5201 pt/cm3 (SD: 807), ranging between 3735 and 6588 pt/cm3. The two individual adjusted campaign-specific means at 27 residential sites were weakly correlated (Spearman's correlation 0.11) and had an intra-class correlation coefficient of 0.06 (95%-confidence interval: -0.18, 0.28). Daily PNC at the reference site was highly correlated (R = 0.64-0.84) with PNC monitored at seven fixed-site stations throughout the city. We observed a seasonal trend at the reference site with the highest PNC in spring. Our measurement campaign revealed that fa & ccedil;ade-level PNC at residences in Copenhagen in 2021-2022 was relatively low with small spatial variability. The large variability in time suggests possibly longer and more frequent measurement campaigns to obtain more stable annual averages. Our study illustrates the challenges of UFP long-term exposure assessment.

Black Smoke (BS) served and still serves in several countries as official measure for Particulate Matter (PM) because of its easy determination via the blackness of filter samples. However, BS is a poor proxy for total PM but a rather good equivalency quantity for light absorbing "Elemental Carbon" deriving from diesel emissions (ten Brink et al., 2021). The latest WHO Report on Air Quality (WHO, 2021) recommends monitoring of Elemental Carbon (EC), but does not prescribe a specific measurement method, so using the BS method to obtain EC concentrations remains an inexpensive option in countries where BS is still measured and expensive direct EC measurements are not easily available. Yet there is a caveat: national regulations demand the use of the measurement protocol given in ISO-9835 (ISO, 1993), which prescribes optical reflectance measurements on filter samples. We analysed this report and found a series of critical issues. It only mentions a dimensionless "Black Smoke Index" (BSI), whereas in official legislation BS must be provided as mass concentration (in μg m-3). In addition ISO-9835 gives no reference for the conversion of the measured light absorbance to BSI; in fact the report does not even provide a proper definition of BS. Therefore we strongly argue to use the official EU-manual by Christolis et al. (1992) for BS determination when and where ever BS is still measured. This manual prescribes the conversion of light absorption as measured with the standard EEL 43 Smoke Stain Reflectometer for samples on the standard substrate of Whatman-1 cellulose fibre filters in monitoring networks. The BS concentrations obtained following this manual can then be provisionally converted to EC with the factor of 0.15 ± 25 % found by ten Brink et al. (2021) to obtain EC values as if measured according to the European reference EUSAAR2-TOT protocol (EN 16909:2017) in the countries still measuring BS until local equivalency is established with proper protocols.

There is limited study from low-and-middle income countries on the effect of perinatal exposure to air pollution and the risk of infection in infant. We assessed the association between perinatal exposure to traffic related air pollution and the risk of infection in infant during their first six months of life. A prospective cohort study was performed in Jakarta, March 2016–September 2020 among 298 mother-infant pairs. PM2.5, soot, NOx, and NO2 concentrations were assessed using land use regression models (LUR) at individual level. Repeated interviewer-administered questionnaires were used to obtain data on infection at 1, 2, 4 and 6 months of age. The infections were categorized as upper respiratory tract (runny nose, cough, wheezing or shortness of breath), lower respiratory tract (pneumonia, bronchiolitis) or gastrointestinal tract infection. Logistic regression models adjusted for covariates were used to assess the association between perinatal exposure to air pollution and the risk of infection in the first six months of life. The average concentrations of PM2.5 and NO2 were much higher than the WHO recommended levels. Upper respiratory tract infections (URTI) were much more common in the first six months of life than diagnosed lower respiratory tract or gastro-intestinal infections (35.6

Road traffic is an important source of noise and air pollution. Modelling of air pollution and noise therefore requires detailed information on annual average daily traffic (AADT) flows on all roads. Europe-wide estimates on traffic intensity are however not publicly available. This has hampered previous Europe-wide air pollution and noise modelling, used extensively in Europe-wide epidemiological studies of morbidity and mortality. We aim to estimate Europe-wide AADT and quantify potential improvements of previous Europe-wide air pollution models. We built separate random forests (RF) models for different road types in OpenStreetMap (highway, primary, secondary and tertiary, and residential roads). We collected observations on annual average daily traffic (AADT) from six European countries. We evaluated our AADT models using 5-fold cross-validation (CV) and by comparison of our Europe-wide traffic flow estimates with national traffic model estimates for Switzerland and the Netherlands. We evaluated whether adding our estimated AADT as predictors for Europe-wide air pollution models trained by more than 2000 routine monitoring sites improved the performance of the models based upon major road length in different buffer sizes. The 5-fold cross-validation result showed our estimates overall captured variations in AADT between road types (R2 2 = 0.82). Our result showed variability in AADT within and between road types, documenting the benefit of our model framework at a continental scale. Our AADT estimates modestly improved model performance of previous Europe-wide air pollution models for NO2, 2 , PM10, 10 , PM2.5, 2.5 , and O3, 3 , especially for NO2 2 (3% improvement of geographically-weighted regression model). Improvement of model performance was larger in urban areas (5% and 8% increases in R2 2 for NO2 2 and O3). 3 ). Importantly, more detailed intra-city near-road variations were captured for traffic-related air pollution. The resulting AADT estimates of all roads across Europe will be useful for further improving air pollution modelling and facilitating harmonized road traffic noise modelling in Europe.

End-stage kidney disease (ESKD) poses a high burden on patients and health systems. While numerous studies indicate an association between air pollution and chronic kidney disease, studies on ESKD are rare. We investigated the association of long-term exposure to nitrogen dioxide (NO₂), fine particulate matter (PM2.5), black carbon (BC) and ozone (O3) with ESKD incidence in two large population-based European cohorts. We followed individuals in the Austrian Vorarlberg Health Monitoring and Promotion Program (VHM&PP) and the Italian Rome Longitudinal Study (RoLS) using dialysis and kidney transplant registries. Long-term exposure to pollutants was estimated at the home address using Europe-wide land use regression models at 100x100m scale. Hazard ratios (HR) were determined from Cox-proportional hazard models adjusted for individual and neighbourhood level confounders. We observed 501 events among 136,823 individuals in VHM&PP (mean age 42.1 years; crude incidence rate (IR) 0.14 per 1000 person-years) and 3231 events among 1,939,461 individuals in RoLS (mean age 52.4 years; IR 0.22 per 1000 person-years). In VHM&PP, there was no evidence of an association between PM2.5 or O3 and ESKD. There were elevated HRs but with large confidence intervals for BC (HR 1.17 [95 % confidence interval (CI): 0.98, 1.39] for 0.5*10-5/m), and for NO₂ (HR 1.14 [95%CI: 0.96, 1.35] for 10 μg/m3). In RoLS, ESKD was associated with PM2.5 (HR 1.37 [95 % CI: 1.06, 1.76] for an increase of 5 μg/m3), while there was no evidence of association with BC, NO2, or O3 exposure. Our study suggests an association of air pollution with ESKD incidence, which differed between the two cohorts and may possibly be influenced by respective air pollution mixtures.

Epidemiological studies of long-term exposure to outdoor air pollution have consistently documented associations with morbidity and mortality. Air pollution exposure in these epidemiological studies is generally assessed at the residential address, because individual time-activity patterns are seldom known in large epidemiological studies. Ignoring time-activity patterns may result in bias in epidemiological studies. The aims of this paper are to assess the agreement between exposure assessed at the residential address and exposures estimated with time-activity integrated and the potential bias in epidemiological studies when exposure is estimated at the residential address. We reviewed exposure studies that have compared residential and time-activity integrated exposures, with a focus on the correlation. We further discuss epidemiological studies that have compared health effect estimates between the residential and time-activity integrated exposure and studies that have indirectly estimated the potential bias in health effect estimates in epidemiological studies related to ignoring time-activity patterns. A large number of studies compared residential and time-activity integrated exposure, especially in Europe and North America, mostly focusing on differences in level. Eleven of these studies reported correlations, showing that the correlation between residential address-based and time-activity integrated long-term air pollution exposure was generally high to very high (R > 0.8). For individual subjects large differences were found between residential and time-activity integrated exposures. Consistent with the high correlation, five of six identified epidemiological studies found nearly identical health effects using residential and time-activity integrated exposure. Six additional studies in Europe and North America showed only small to moderate potential bias (9 to 30

BACKGROUND:Long-term exposure to ambient air pollution has been linked with all-cause mortality and cardiovascular and respiratory diseases. Suggestive associations between ambient air pollutants and neurodegeneration have also been reported, but due to the small effect and relatively rare outcomes evidence is yet inconclusive. Our aim was to investigate the associations between long-term air pollution exposure and mortality from neurodegenerative diseases. METHODS:A Dutch national cohort of 10.8 million adults aged ≥30 years was followed from 2013 until 2019. Annual average concentrations of air pollutants (ultra-fine particles (UFP), nitrogen dioxide (NO2), fine particles (PM2.5 and PM10) and elemental carbon (EC)) were estimated at the home address at baseline, using land-use regression models. The outcome variables were mortality due to amyotrophic lateral sclerosis (ALS), Parkinson's disease, non-vascular dementia, Alzheimer's disease, and multiple sclerosis (MS). Hazard ratios (HR) were estimated using Cox models, adjusting for individual and area-level socio-economic status covariates. RESULTS:We had a follow-up of 71 million person-years. The adjusted HRs for non-vascular dementia were significantly increased for NO2 (1.03; 95% confidence interval (CI) 1.02-1.05) and PM2.5 (1.02; 95%CI 1.01-1.03) per interquartile range (IQR; 6.52 and 1.47 μg/m3, respectively). The association with PM2.5 was also positive for ALS (1.02; 95%CI 0.97-1.07). These associations remained positive in sensitivity analyses and two-pollutant models. UFP was not associated with any outcome. No association with air pollution was found for Parkinson's disease and MS. Inverse associations were found for Alzheimer's disease. CONCLUSION:Our findings, using a cohort of more than 10 million people, provide further support for associations between long-term exposure to air pollutants (PM2.5 and particularly NO2) and mortality of non-vascular dementia. No associations were found for Parkinson and MS and an inverse association was observed for Alzheimer's disease.

BACKGROUND:This study examines longitudinal associations of air pollution and green space with cardiometabolic risk among children in the Netherlands. METHODS:Three Dutch prospective cohorts with a total of 13,822 participants aged 5 to 17 years were included: (1) the Amsterdam Born Children and their Development (ABCD) study from Amsterdam (n = 2,547), (2) the Generation R study from Rotterdam (n = 5,431), and (3) the Lifelines study from northern Netherlands (n = 5,844). Air pollution (PM2.5, PM10, NO2, and elemental carbon (EC)) and green space exposures (density in multiple Euclidean buffer sizes) from 2006 to 2017 at home address level were used. Cardiometabolic risk factor clustering was assessed by a MetScore, which was derived from a confirmatory factor analysis of six cardiometabolic risk factors to assess the overall risk. Linear regression models with change in Metscore as the dependent variable, adjusted for multiple confounders, were conducted for each cohort separately. Meta-analyses were used to pool cohort-specific estimates. RESULTS:Exposure to higher levels of NO2 and EC was significantly associated with increases in MetScore in Lifelines (per SD higher exposure: βNO2 = 0.006, 95 % CI = 0.001 to 0.010; βEC = 0.008, 95 % CI = 0.002 to 0.014). In the other two cohort studies, these associations were in the same direction but these were not significant. Higher green space density in 500-meter buffer zones around participants' residential addresses was not significantly associated with decreases of MetScore in all three cohorts. Higher green space density in 2000-meter buffer zones was significantly associated with decreases of MetScore in ABCD and Lifelines (per SD higher green space density: βABCD = -0.008, 95 % CI = -0.013 to -0.003; βLifelines = -0.002, 95 % CI = -0.003 to -0.00003). The pooled estimates were βNO2 = 0.003 (95 % CI = -0.001 to 0.006) for NO2, βEC = 0.003 (95 % CI = -0.001, 0.007) for EC, and β500m buffer = -0.0014 (95 % CI = -0.0026 to -0.0001) for green space. CONCLUSIONS:More green space exposure at residence was associated with decreased cardiometabolic risk in children. Exposure to more NO2 and EC was also associated with increased cardiometabolic risk.

Background: Studies have linked air pollution to lung cancer incidence and mortality, but few have compared these associations, which may differ due to cancer survival variations. We aimed to evaluate the association between long-term air pollution exposure and lung cancer incidence and compare findings with previous lung cancer mortality analyses within the same cohorts. Methods: We analyzed four population-based administrative cohorts in Denmark (2000-2015), England (2011-2017), Norway (2001-2016) and Rome (2001-2015). We assessed residential exposure to annual average fine particulate matter (PM2.5), nitrogen dioxide (NO2), black carbon (BC), and warm-season ozone (O3) using Europe-wide land use regression models. We used Cox proportional hazard models to evaluate cohort-specific hazard ratios (HRs) and 95% confidence intervals (CIs) for lung cancer incidence identified using hospital admission records (English and Roman cohorts) or cancer registries (Danish and Norwegian cohorts). We evaluated the associations at low exposure levels using subset analyses and natural cubic splines. Cohort-specific HRs were pooled using random-effects meta-analyses, separately for incidence and mortality. Results: Over 93,733,929 person-years of follow-up, 111,949 incident lung cancer cases occurred. Incident lung cancer was positively associated with PM2.5, NO2 and BC, and negatively associated with O3. The negative O3

Air pollution exposure is typically assessed at the front door where people live in large-scale epidemiological studies, overlooking individuals' daily mobility out-of-home. However, there is limited evidence that incorporating mobility data into personal air pollution assessment improves exposure assessment compared to home-based assessments. This study aimed to compare the agreement between mobility-based and home-based assessments with personal exposure measurements. We measured repeatedly particulate matter (PM2.5) and black carbon (BC) using a sample of 41 older adults in the Netherlands. In total, 104 valid 24 h average personal measurements were collected. Home-based exposures were estimated by combining participants' home locations and temporal-adjusted air pollution maps. Mobility-based estimates of air pollution were computed based on smartphone-based tracking data, temporal-adjusted air pollution maps, indoor-outdoor penetration, and travel mode adjustment. Intraclass correlation coefficients (ICC) revealed that mobility-based estimates significantly improved agreement with personal measurements compared to home-based assessments. For PM2.5, agreement increased by 64% (ICC: 0.39-0.64), and for BC, it increased by 21% (ICC: 0.43-0.52). Our findings suggest that adjusting for indoor-outdoor pollutant ratios in mobility-based assessments can provide more valid estimates of air pollution than the commonly used home-based assessments, with no added value observed from travel mode adjustments.

Background Air pollution contributes to a large disease burden and some populations are disproportionately exposed. We aimed to evaluate ethnic and socioeconomic differences in exposure to air pollution in the Netherlands. Methods We did a nationwide, cross-sectional analysis of all residents of the Netherlands on Jan 1, 2019. Sociodemographic information was centralised by Statistics Netherlands and mainly originated from the National Population Register, the tax register, and education registers. Concentrations of NO2, PM2 center dot 5, PM10, and elemental carbon, modelled by the National Institute for Public Health and the Environment, were linked to the individual-level demographic data. We assessed differences in air pollution exposures across the 40 largest minority ethnic groups. Evaluation of how ethnicity intersected with socioeconomic position in relation to exposures was done for the ten largest ethnic groups, plus Chinese and Indian groups, in both urban and rural areas using multivariable linear regression analyses. Findings The total study population consisted of 17251511 individuals. Minority ethnic groups were consistently exposed to higher levels of air pollution than the ethnic Dutch population. The magnitude of inequalities varied between the minority ethnic groups, with 3-44% higher exposures to NO2 and 1-9% higher exposures to PM2 center dot 5 compared with the ethnic Dutch group. Average exposures were highest for the lowest socioeconomic group. Ethnic inequalities in exposure remained after adjustment for socioeconomic position and were of similar magnitude in urban and rural areas. Interpretation The variability in air pollution exposure across ethnic and socioeconomic subgroups in the Netherlands indicates environmental injustice at the intersection of social characteristics. The health consequences of the observed inequalities and the underlying processes driving them warrant further investigation. Copyright (c) 2023 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license