Long-term ambient ozone, omega-3 fatty acid, genetic susceptibility, and risk of mental disorders among middle-aged and older adults in UK biobank.

BACKGROUND: Evidence linking ozone to depression and anxiety disorders remains sparse and results are heterogeneous. It remains unknown whether omega-3 fatty acid, or genetic susceptibility of mental disorders modify the impacts of ozone. The aim is to assess the associations of ambient ozone with depression and anxiety, and further explore the potential modification effects of omega-3 fatty acid and genetic susceptibility. METHODS: In total of 257,534 participants were enrolled from 2006 to 2010 and followed up to 2016. Depression and anxiety were assessed using mental health questionnaires, primary care records and hospital admission records. The annual average concentrations of ozone were calculated and linked to individuals by home address. Dietary intake and plasma concentration were selected to reflect levels of omega-3 fatty acid. Polygenetic risk scores were selected to reflect genetic susceptibility. We examined the associations of ozone and incident mental disorders, and potential modification of omega-3 fatty acid and genetic susceptibility. RESULTS: Incidences of depression (N = 6957) and anxiety (N = 6944) was associated with increase of ozone. Higher levels of omega-3 fatty acid might attenuate the ozone related depression risk. However, the modification effects of genetic susceptibility were not found. CONCLUSIONS: Long-term exposure to ambient ozone increase the risk of mental disorders among the middle aged and older adults, and omega-3 fatty acid could reduce the adverse effects of ozone on mental health. Higher intake of omega-3 fatty acid is a potential strategy to prevent the risks caused by ozone on public mental health.

Emulation and Sensitivity Analysis of the Community Multiscale Air Quality Model for a UK Ozone Pollution Episode.

Gaussian process emulation techniques have been used with the Community Multiscale Air Quality model, simulating the effects of input uncertainties on ozone and NO2 output, to allow robust global sensitivity analysis (SA). A screening process ranked the effect of perturbations in 223 inputs, isolating the 30 most influential from emissions, boundary conditions (BCs), and reaction rates. Community Multiscale Air Quality (CMAQ) simulations of a July 2006 ozone pollution episode in the UK were made with input values for these variables plus ozone dry deposition velocity chosen according to a 576 point Latin hypercube design. Emulators trained on the output of these runs were used in variance-based SA of the model output to input uncertainties. Performing these analyses for every hour of a 21 day period spanning the episode and several days on either side allowed the results to be presented as a time series of sensitivity coefficients, showing how the influence of different input uncertainties changed during the episode. This is one of the most complex models to which these methods have been applied, and here, they reveal detailed spatiotemporal patterns of model sensitivities, with NO and isoprene emissions, NO2 photolysis, ozone BCs, and deposition velocity being among the most influential input uncertainties.

Effect of Exhaust- and Nonexhaust-Related Components of Particulate Matter on Long-Term Survival After Stroke.

BACKGROUND AND PURPOSE: Outdoor air pollution represents a potentially modifiable risk factor for stroke. We examined the link between ambient pollution and mortality up to 5 years poststroke, especially for pollutants associated with vehicle exhaust. METHODS: Data from the South London Stroke Register, a population-based register covering an urban, multiethnic population, were used. Hazard ratios (HR) for a 1 interquartile range increase in particulate matter <2.5 µm diameter (PM2.5) and PM <10 µm (PM10) were estimated poststroke using Cox regression, overall and broken down into exhaust and nonexhaust components. Analysis was stratified for ischemic and hemorrhagic strokes and was further broken down by Oxford Community Stroke Project classification. RESULTS: The hazard of death associated with PM2.5 up to 5 years after stroke was significantly elevated (P=0.006) for all strokes (HR=1.28; 95% confidence interval [CI], 1.08-1.53) and ischemic strokes (HR, 1.32; 95% CI, 1.08-1.62). Within ischemic subtypes, PM2.5 pollution increased mortality risk for total anterior circulation infarcts by 2-fold (HR, 2.01; 95% CI, 1.17-3.48; P=0.012) and by 78% for lacunar infarcts (HR, 1.78; 95% CI, 1.18-2.66; P=0.006). PM10 pollution was associated with 45% increased mortality risk for lacunar infarct strokes (HR, 1.45; 95% CI, 1.06-2.00; P=0.022). Separating PM2.5 and PM10 into exhaust and nonexhaust components did not show increased mortality. CONCLUSIONS: Exposure to certain outdoor PM pollution, particularly PM2.5, increased mortality risk poststroke up to 5 years after the initial stroke.

Spatially resolved flux measurements of NOx from London suggest significantly higher emissions than predicted by inventories.

To date, direct validation of city-wide emissions inventories for air pollutants has been difficult or impossible. However, recent technological innovations now allow direct measurement of pollutant fluxes from cities, for comparison with emissions inventories, which are themselves commonly used for prediction of current and future air quality and to help guide abatement strategies. Fluxes of NOx were measured using the eddy-covariance technique from an aircraft flying at low altitude over London. The highest fluxes were observed over central London, with lower fluxes measured in suburban areas. A footprint model was used to estimate the spatial area from which the measured emissions occurred. This allowed comparison of the flux measurements to the UK's National Atmospheric Emissions Inventory (NAEI) for NOx, with scaling factors used to account for the actual time of day, day of week and month of year of the measurement. The comparison suggests significant underestimation of NOx emissions in London by the NAEI, mainly due to its under-representation of real world road traffic emissions. A comparison was also carried out with an enhanced version of the inventory using real world driving emission factors and road measurement data taken from the London Atmospheric Emissions Inventory (LAEI). The measurement to inventory agreement was substantially improved using the enhanced version, showing the importance of fully accounting for road traffic, which is the dominant NOx emission source in London. In central London there was still an underestimation by the inventory of 30-40% compared with flux measurements, suggesting significant improvements are still required in the NOx emissions inventory.

London Hybrid Exposure Model: Improving Human Exposure Estimates to NO2 and PM2.5 in an Urban Setting.

Here we describe the development of the London Hybrid Exposure Model (LHEM), which calculates exposure of the Greater London population to outdoor air pollution sources, in-buildings, in-vehicles, and outdoors, using survey data of when and where people spend their time. For comparison and to estimate exposure misclassification we compared Londoners LHEM exposure with exposure at the residential address, a commonly used exposure metric in epidemiological research. In 2011, the mean annual LHEM exposure to outdoor sources was estimated to be 37% lower for PM2.5 and 63% lower for NO2 than at the residential address. These decreased estimates reflect the effects of reduced exposure indoors, the amount of time spent indoors (∼95%), and the mode and duration of travel in London. We find that an individual's exposure to PM2.5 and NO2 outside their residential address is highly correlated (Pearson's R of 0.9). In contrast, LHEM exposure estimates for PM2.5 and NO2 suggest that the degree of correlation is influenced by their exposure in different transport modes. Further development of the LHEM has the potential to increase the understanding of exposure error and bias in time-series and cohort studies and thus better distinguish the independent effects of NO2 and PM2.5.

Road traffic noise is associated with increased cardiovascular morbidity and mortality and all-cause mortality in London.

AIMS: Road traffic noise has been associated with hypertension but evidence for the long-term effects on hospital admissions and mortality is limited. We examined the effects of long-term exposure to road traffic noise on hospital admissions and mortality in the general population. METHODS AND RESULTS: The study population consisted of 8.6 million inhabitants of London, one of Europe's largest cities. We assessed small-area-level associations of day- (7:00-22:59) and nighttime (23:00-06:59) road traffic noise with cardiovascular hospital admissions and all-cause and cardiovascular mortality in all adults (≥25 years) and elderly (≥75 years) through Poisson regression models. We adjusted models for age, sex, area-level socioeconomic deprivation, ethnicity, smoking, air pollution, and neighbourhood spatial structure. Median daytime exposure to road traffic noise was 55.6 dB. Daytime road traffic noise increased the risk of hospital admission for stroke with relative risk (RR) 1.05 [95% confidence interval (CI): 1.02-1.09] in adults, and 1.09 (95% CI: 1.04-1.14) in the elderly in areas >60 vs. <55 dB. Nighttime noise was associated with stroke admissions only among the elderly. Daytime noise was significantly associated with all-cause mortality in adults [RR 1.04 (95% CI: 1.00-1.07) in areas >60 vs. <55 dB]. Positive but non-significant associations were seen with mortality for cardiovascular and ischaemic heart disease, and stroke. Results were similar for the elderly. CONCLUSIONS: Long-term exposure to road traffic noise was associated with small increased risks of all-cause mortality and cardiovascular mortality and morbidity in the general population, particularly for stroke in the elderly.

Measurement of NO(x) fluxes from a tall tower in Central London, UK and comparison with emissions inventories.

Direct measurements of NOx concentration and flux were made from a tall tower in central London, UK as part of the Clean Air for London (ClearfLo) project. Fast time resolution (10 Hz) NO and NO2 concentrations were measured and combined with fast vertical wind measurements to provide top-down flux estimates using the eddy covariance technique. Measured NOx fluxes were usually positive and ranged from close to zero at night to 2000-8000 ng m(-2) s(-1) during the day. Peak fluxes were usually observed in the morning, coincident with the maximum traffic flow. Measurements of the NOx flux have been scaled and compared to the UK National Atmospheric Emissions Inventory (NAEI) estimate of NOx emission for the measurement footprint. The measurements are on average 80% higher than the NAEI emission inventory for all of London. Observations made in westerly airflow (from parts of London where traffic is a smaller fraction of the NOx source) showed a better agreement on average with the inventory. The observations suggest that the emissions inventory is poorest at estimating NOx when traffic is the dominant source, in this case from an easterly direction from the BT Tower. Agreement between the measurements and the London Atmospheric Emissions Inventory (LAEI) are better, due to the more explicit treatment of traffic flow by this more detailed inventory. The flux observations support previous tailpipe observations of higher NOx emitted from the London vehicle diesel fleet than is represented in the NAEI or predicted for several EURO emission control technologies. Higher-than-anticipated vehicle NOx is likely responsible for the significant discrepancies that exist in London between observed NOx and long-term NOx projections.

Outdoor air pollution, subtypes and severity of ischemic stroke--a small-area level ecological study.

BACKGROUND: Evidence linking outdoor air pollution and incidence of ischemic stroke subtypes and severity is limited. We examined associations between outdoor PM10 and NO2 concentrations modeled at a fine spatial resolution and etiological and clinical ischemic stroke subtypes and severity of ischemic stroke. METHODS: We used a small-area level ecological study design and a stroke register set up to capture all incident cases of first ever stroke (1995-2007) occurring in a defined geographical area in South London (948 census output areas; population of 267839). Modeled PM10 and NO2 concentrations were available at a very fine spatial scale (20 meter by 20 meter grid point resolution) and were aggregated to output area level using postcode population weighted averages. Ischemic stroke was classified using the Oxford clinical classification, the Trial of Org 10172 in Acute Stroke Treatment (TOAST) etiological classification, National Institutes of Health Stroke Scale (NIHSS) score and a pragmatic clinical severity classification based on Glasgow coma score, ability to swallow, urinary continence and death <2 days of stroke onset. RESULTS: Mean (SD) concentrations were 25.1 (1.2) ug/m(3) (range 23.3-36.4) for PM10 and 41.4 (3.0) ug/m(3) (range 35.4-68.0) for NO2. There were 2492 incident cases of ischemic stroke. We found no evidence of association between these pollutants and the incidence of ischemic stroke subtypes classified using the Oxford and TOAST classifications. We found no significant association with stroke severity using NIHSS severity categories. However, we found that outdoor concentrations of both PM10 and NO2 appeared to be associated with increased incidence of mild but not severe ischemic stroke, classified using the pragmatic clinical severity classification. For mild ischemic stroke, the rate ratio in the highest PM10 category by tertile was 1.20 (1.05-1.38) relative to the lowest category. The rate ratio in the highest NO2 category was 1.22 (1.06-1.40) relative to the lowest category. CONCLUSIONS: We found no evidence of association between outdoor PM10 and NO2 concentrations and ischemic stroke subtypes but there was a suggestion that living in areas with elevated outdoor PM10 and NO2 concentrations might be associated with increased incidence of mild, but not severe, ischemic stroke.

Can the UK meet the World Health Organization PM2.5 interim target of 10 µg m-3 by 2030?

The recent United Kingdom (UK) Environment Act consultation had the intention of setting two targets for PM2.5 (particles with an aerodynamic diameter less than 2.5 µm), one related to meeting an annual average concentration and the second to reducing population exposure. As part of the consultation, predictions of PM2.5 concentrations in 2030 were made by combining European Union (EU) and UK government's emissions forecasts, with the Climate Change Committee's (CCC) Net Zero vehicle forecasts, and in London with the addition of local policies based on the London Environment Strategy (LES). Predictions in 2018 showed 6.4% of the UK's area and 82.6% of London's area had PM2.5 concentrations above the World Health Organization (WHO) interim target of 10 µg m-3, but by 2030, over 99% of the UK's area was predicted to be below it. However, kerbside concentrations in London and other major cities were still at risk of exceeding 10 µg m-3. With local action on PM2.5 in London, population weighted concentrations showed full compliance with the WHO interim target of 10 µg m-3 in 2030. However, predicting future PM2.5 concentrations and interpreting the results will always be difficult and uncertain for many reasons, such as imperfect models and the difficulty in estimating future emissions. To help understand the sensitivity of the model's PM2.5 predictions in 2030, current uncertainty was quantified using PM2.5 measurements and showed large areas in the UK that were still at risk of exceeding the WHO interim target despite the model predictions being below 10 µg m-3. Our results do however point to the benefits that policy at EU, UK and city level can have on achieving the WHO interim target of 10 µg m-3. These results were submitted to the UK Environment Act consultation. Nevertheless, the issues addressed here could be applicable to other European cities.

Outdoor air pollution and incidence of ischemic and hemorrhagic stroke: a small-area level ecological study.

BACKGROUND AND PURPOSE: Evidence linking outdoor air pollution and incidence of stroke is limited. We examined effects of outdoor air pollution on the incidence of ischemic and hemorrhagic stroke at the population level focusing on middle-aged and older people. METHODS: We used a small-area level ecological study design and a stroke register set up to capture all incident cases of first-ever stroke occurring in a defined geographical area in south London (948 census output areas) where road traffic contributes to spatial variation in air pollution. Population-weighted averages were calculated for output areas using outdoor nitrogen dioxide and PM(10) concentrations modeled at a 20-m resolution. RESULTS: There were 1832 ischemic and 348 hemorrhagic strokes in 1995 to 2004 occurring among a resident population of 267 839. Mean (SD) concentration was 25.1 (1.2) µg/m(3) (range, 23.3-36.4 µg/m(3)) for PM(10) and 41.4 (3.0) µg/m(3) (range, 35.4-68.0 µg/m(3)) for nitrogen dioxide. For ischemic stroke, adjusted rate ratios per 10-µg/m(3) increase, for all ages, 40 to 64 and 65 to 79 years, respectively, were 1.22 (0.77-1.93), 1.12 (0.55-2.28), and 1.86 (1.10-3.13) for PM(10) and 1.11 (0.93-1.32), 1.13 (0.86-1.50), and 1.23 (0.99-1.53) for nitrogen dioxide. For hemorrhagic stroke, the corresponding rate ratios were 0.52 (0.20-1.37), 0.78 (0.17-3.51), and 0.51 (0.12-2.22) for PM(10) and 0.86 (0.60-1.24), 1.12 (0.66-1.90), and 0.78 (0.44-1.39) for nitrogen dioxide. CONCLUSIONS: Although there was no significant association between outdoor air pollutants and ischemic stroke incidence for all ages combined, there was a suggestion of increased risk among people aged 65 to 79 years. There was no evidence of increased incidence in hemorrhagic stroke.

Modeling exposures to the oxidative potential of PM10.

Differences in the toxicity of ambient particulate matter (PM) due to varying particle composition across locations may contribute to variability in results from air pollution epidemiologic studies. Though most studies have used PM mass concentration as the exposure metric, an alternative which accounts for particle toxicity due to varying particle composition may better elucidate whether PM from specific sources is responsible for observed health effects. The oxidative potential (OP) of PM < 10 µm (PM(10)) was measured as the rate of depletion of the antioxidant reduced glutathione (GSH) in a model of human respiratory tract lining fluid. Using a database of GSH OP measures collected in greater London, U.K. from 2002 to 2006, we developed and validated a predictive spatiotemporal model of the weekly GSH OP of PM(10) that included geographic predictors. Predicted levels of OP were then used in combination with those of weekly PM(10) mass to estimate exposure to PM(10) weighted by its OP. Using cross-validation (CV), brake and tire wear emissions of PM(10) from traffic within 50 m and tailpipe emissions of nitrogen oxides from heavy-goods vehicles within 100 m were important predictors of GSH OP levels. Predictive accuracy of the models was high for PM(10) (CV R(2)=0.83) but only moderate for GSH OP (CV R(2) = 0.44) when comparing weekly levels; however, the GSH OP model predicted spatial trends well (spatial CV R(2) = 0.73). Results suggest that PM(10) emitted from traffic sources, specifically brake and tire wear, has a higher OP than that from other sources, and that this effect is very local, occurring within 50-100 m of roadways.

One way coupling of CMAQ and a road source dispersion model for fine scale air pollution predictions.

In this paper we have coupled the CMAQ and ADMS air quality models to predict hourly concentrations of NO X , NO2 and O3 for London at a spatial scale of 20 m × 20 m. Model evaluation has demonstrated reasonable agreement with measurements from 80 monitoring sites in London. For NO2 the model evaluation statistics gave 73% of the hourly concentrations within a factor of two of observations, a mean bias of -4.7 ppb and normalised mean bias of -0.17, a RMSE value of 17.7 and an r value of 0.58. The equivalent results for O3 were 61% (FAC2), 2.8 ppb (MB), 0.15 (NMB), 12.1 (RMSE) and 0.64 (r). Analysis of the errors in the model predictions by hour of the week showed the need for improvements in predicting the magnitude of road transport related NO X emissions as well as the hourly emissions scaling in the model. These findings are consistent with recent evidence of UK road transport NO X emissions, reported elsewhere. The predictions of wind speed using the WRF model also influenced the model results and contributed to the daytime over prediction of NO X concentrations at the central London background site at Kensington and Chelsea. An investigation of the use of a simple NO-NO2-O3 chemistry scheme showed good performance close to road sources, and this is also consistent with previous studies. The coupling of the two models raises an issue of emissions double counting. Here, we have put forward a pragmatic solution to this problem with the result that a median double counting error of 0.42% exists across 39 roadside sites in London. Finally, whilst the model can be improved, the current results show promise and demonstrate that the use of a combination of regional scale and local scale models can provide a practical modelling tool for policy development at intergovernmental, national and local authority level, as well as for use in epidemiological studies.

Association of Air Pollution Exposure in Childhood and Adolescence With Psychopathology at the Transition to Adulthood.

Importance: Air pollution exposure damages the brain, but its associations with the development of psychopathology are not fully characterized. Objective: To assess whether air pollution exposure in childhood and adolescence is associated with greater psychopathology at 18 years of age. Design, Setting, and Participants: The Environmental-Risk Longitudinal Twin Study is a population-based cohort study of 2232 children born from January 1, 1994, to December 4, 1995, across England and Wales and followed up to 18 years of age. Pollution data generation was completed on April 22, 2020; data were analyzed from April 27 to July 31, 2020. Exposures: High-resolution annualized estimates of outdoor nitrogen oxides (NOx) and particulate matter with aerodynamic diameter less than 2.5 µm (PM2.5) linked to home addresses at the ages of 10 and 18 years and then averaged. Main Outcomes and Measures: Mental health disorder symptoms assessed through structured interview at 18 years of age and transformed through confirmatory factor analysis into continuous measures of general psychopathology (primary outcome) and internalizing, externalizing, and thought disorder symptoms (secondary outcomes) standardized to a mean (SD) of 100 (15). Hypotheses were formulated after data collection, and analyses were preregistered. Results: A total of 2039 participants (1070 [52.5%] female) had full data available. After adjustment for family and individual factors, each interquartile range increment increase in NOx exposure was associated with a 1.40-point increase (95% CI, 0.41-2.38; P = .005) in general psychopathology. There was no association between continuously measured PM2.5 and general psychopathology (b = 0.45; 95% CI, -0.26 to 1.11; P = .22); however, those in the highest quartile of PM2.5 exposure scored 2.04 points higher (95% CI, 0.36-3.72; P = .02) than those in the bottom 3 quartiles. Copollutant models, including both NOx and PM2.5, implicated NOx alone in these significant findings. NOx exposure was associated with all secondary outcomes, although associations were weakest for internalizing (adjusted b = 1.07; 95% CI, 0.10-2.04; P = .03), medium for externalizing (adjusted b = 1.42; 95% CI, 0.53-2.31; P = .002), and strongest for thought disorder symptoms (adjusted b = 1.54; 95% CI, 0.50-2.57; P = .004). Despite NOx concentrations being highest in neighborhoods with worse physical, social, and economic conditions, adjusting estimates for neighborhood characteristics did not change the results. Conclusions and Relevance: Youths exposed to higher levels of outdoor NOx experienced greater psychopathology at the transition to adulthood. Air pollution may be a nonspecific risk factor for the development of psychopathology.

Childhood exposure to ambient air pollution and predicting individual risk of depression onset in UK adolescents.

Knowledge about early risk factors for major depressive disorder (MDD) is critical to identify those who are at high risk. A multivariable model to predict adolescents' individual risk of future MDD has recently been developed however its performance in a UK sample was far from perfect. Given the potential role of air pollution in the aetiology of depression, we investigate whether including childhood exposure to air pollution as an additional predictor in the risk prediction model improves the identification of UK adolescents who are at greatest risk for developing MDD. We used data from the Environmental Risk (E-Risk) Longitudinal Twin Study, a nationally representative UK birth cohort of 2232 children followed to age 18 with 93% retention. Annual exposure to four pollutants - nitrogen dioxide (NO2), nitrogen oxides (NOX), particulate matter <2.5 µm (PM2.5) and <10 µm (PM10) - were estimated at address-level when children were aged 10. MDD was assessed via interviews at age 18. The risk of developing MDD was elevated most for participants with the highest (top quartile) level of annual exposure to NOX (adjusted OR = 1.43, 95% CI = 0.96-2.13) and PM2.5 (adjusted OR = 1.35, 95% CI = 0.95-1.92). The separate inclusion of these ambient pollution estimates into the risk prediction model improved model specificity but reduced model sensitivity - resulting in minimal net improvement in model performance. Findings indicate a potential role for childhood ambient air pollution exposure in the development of adolescent MDD but suggest that inclusion of risk factors other than this may be important for improving the performance of the risk prediction model.

Impact of outdoor air pollution on survival after stroke: population-based cohort study.

BACKGROUND AND PURPOSE: The impact of air pollution on survival after stroke is unknown. We examined the impact of outdoor air pollution on stroke survival by studying a population-based cohort. METHODS: All patients who experienced their first-ever stroke between 1995 and 2005 in a geographically defined part of London, where road traffic contributes to spatial variation in air pollution, were followed up to mid-2006. Outdoor concentrations of nitrogen dioxide and particulate matter <10 microm in diameter modeled at a 20-m grid point resolution for 2002 were linked to residential postal codes. Hazard ratios were adjusted for age, sex, social class, ethnicity, smoking, alcohol consumption, prestroke functional ability, pre-existing medical conditions, stroke subtype and severity, hospital admission, and neighborhood socioeconomic deprivation. RESULTS: There were 1856 deaths among 3320 patients. Median survival was 3.7 years (interquartile range, 0.1 to 10.8). Mean exposure levels were 41 microg/m(3) (SD, 3.3; range, 32.2 to 103.2) for nitrogen dioxide and 25 microg/m(3) (SD, 1.3; range, 22.7 to 52) for particulate matter <10 microm in diameter. A 10-microg/m(3) increase in nitrogen dioxide was associated with a 28% (95% CI, 11% to 48%) increase in risk of death. A 10-microg/m(3) increase in particulate matter <10 mum in diameter was associated with a 52% (6% to 118%) increase in risk of death. Reduced survival was apparent throughout the follow-up period, ruling out short-term mortality displacement. CONCLUSIONS: Survival after stroke was lower among patients living in areas with higher levels of outdoor air pollution. If causal, a 10-microg/m(3) reduction in nitrogen dioxide exposure might be associated with a reduction in mortality comparable to that for stroke units. Improvements in outdoor air quality might contribute to better survival after stroke.

Impacts of air pollution and noise on risk of preterm birth and stillbirth in London.

BACKGROUND: Evidence for associations between ambient air pollution and preterm birth and stillbirth is inconsistent. Road traffic produces both air pollutants and noise, but few studies have examined these co-exposures together and none to date with all-cause or cause-specific stillbirths. OBJECTIVES: To analyse the relationship between long-term exposure to air pollution and noise at address level during pregnancy and risk of preterm birth and stillbirth. METHODS: The study population comprised 581,774 live and still births in the Greater London area, 2006-2010. Outcomes were preterm birth (<37 completed weeks gestation), all-cause stillbirth and cause-specific stillbirth. Exposures during pregnancy to particulate matter with diameter <2.5 µm (PM2.5) and <10 µm (PM10), ozone (O3), primary traffic air pollutants (nitrogen dioxide, nitrogen oxides, PM2.5 from traffic exhaust and traffic non-exhaust), and road traffic noise were estimated based on maternal address at birth. RESULTS: An interquartile range increase in O3 exposure was associated with elevated risk of preterm birth (OR 1.15 95% CI: 1.11, 1.18, for both Trimester 1 and 2), all-cause stillbirth (Trimester 1 OR 1.17 95% CI: 1.07, 1.27; Trimester 2 OR 1.20 95% CI: 1.09, 1.32) and asphyxia-related stillbirth (Trimester 1 OR 1.22 95% CI: 1.01, 1.49). Odds ratios with the other air pollutant exposures examined were null or <1, except for primary traffic non-exhaust related PM2.5, which was associated with 3% increased odds of preterm birth (Trimester 1) and 7% increased odds stillbirth (Trimester 1 and 2) when adjusted for O3. Elevated risk of preterm birth was associated with increasing road traffic noise, but only after adjustment for certain air pollutant exposures. DISCUSSION: Our findings suggest that exposure to higher levels of O3 and primary traffic non-exhaust related PM2.5 during pregnancy may increase risk of preterm birth and stillbirth; and a possible relationship between long-term traffic-related noise and risk of preterm birth. These findings extend and strengthen the evidence base for important public health impacts of ambient ozone, particulate matter and noise in early life.

Comparing the performance of air pollution models for nitrogen dioxide and ozone in the context of a multilevel epidemiological analysis.

Using modeled air pollutant predictions as exposure variables in epidemiological analyses can produce bias in health effect estimation. We used statistical simulation to estimate these biases and compare different air pollution models for London. METHODS: Our simulations were based on a sample of 1,000 small geographical areas within London, United Kingdom. "True" pollutant data (daily mean nitrogen dioxide [NO2] and ozone [O3]) were simulated to include spatio-temporal variation and spatial covariance. All-cause mortality and cardiovascular hospital admissions were simulated from "true" pollution data using prespecified effect parameters for short and long-term exposure within a multilevel Poisson model. We compared: land use regression (LUR) models, dispersion models, LUR models including dispersion output as a spline (hybrid1), and generalized additive models combining splines in LUR and dispersion outputs (hybrid2). Validation datasets (model versus fixed-site monitor) were used to define simulation scenarios. RESULTS: For the LUR models, bias estimates ranged from -56% to +7% for short-term exposure and -98% to -68% for long-term exposure and for the dispersion models from -33% to -15% and -52% to +0.5%, respectively. Hybrid1 provided little if any additional benefit, but hybrid2 appeared optimal in terms of bias estimates for short-term (-17% to +11%) and long-term (-28% to +11%) exposure and in preserving coverage probability and statistical power. CONCLUSIONS: Although exposure error can produce substantial negative bias (i.e., towards the null), combining outputs from different air pollution modeling approaches may reduce bias in health effect estimation leading to improved impact evaluation of abatement policies.

The impact of measurement error in modeled ambient particles exposures on health effect estimates in multilevel analysis: A simulation study.

Various spatiotemporal models have been proposed for predicting ambient particulate exposure for inclusion in epidemiological analyses. We investigated the effect of measurement error in the prediction of particulate matter with diameter <10 µm (PM10) and <2.5 µm (PM2.5) concentrations on the estimation of health effects. METHODS: We sampled 1,000 small administrative areas in London, United Kingdom, and simulated the "true" underlying daily exposure surfaces for PM10 and PM2.5 for 2009-2013 incorporating temporal variation and spatial covariance informed by the extensive London monitoring network. We added measurement error assessed by comparing measurements at fixed sites and predictions from spatiotemporal land-use regression (LUR) models; dispersion models; models using satellite data and applying machine learning algorithms; and combinations of these methods through generalized additive models. Two health outcomes were simulated to assess whether the bias varies with the effect size. We applied multilevel Poisson regression to simultaneously model the effect of long- and short-term pollutant exposure. For each scenario, we ran 1,000 simulations to assess measurement error impact on health effect estimation. RESULTS: For long-term exposure to particles, we observed bias toward the null, except for traffic PM2.5 for which only LUR underestimated the effect. For short-term exposure, results were variable between exposure models and bias ranged from -11% (underestimate) to 20% (overestimate) for PM10 and of -20% to 17% for PM2.5. Integration of models performed best in almost all cases. CONCLUSIONS: No single exposure model performed optimally across scenarios. In most cases, measurement error resulted in attenuation of the effect estimate.

The Lancet Countdown on health benefits from the UK Climate Change Act: a modelling study for Great Britain.

BACKGROUND: Climate change poses a dangerous and immediate threat to the health of populations in the UK and worldwide. We aimed to model different scenarios to assess the health co-benefits that result from mitigation actions. METHODS: In this modelling study, we combined a detailed techno-economic energy systems model (UK TIMES), air pollutant emission inventories, a sophisticated air pollution model (Community Multi-scale Air Quality), and previously published associations between concentrations and health outcomes. We used four scenarios and focused on the air pollution implications from fine particulate matter (PM2·5), nitrogen dioxide (NO2) and ozone. The four scenarios were baseline, which assumed no further climate actions beyond those already achieved and did not meet the UK's Climate Change Act (at least an 80% reduction in carbon dioxide equivalent emissions by 2050 compared with 1990) target; nuclear power, which met the Climate Change Act target with a limited increase in nuclear power; low-greenhouse gas, which met the Climate Change Act target without any policy constraint on nuclear build; and a constant scenario that held 2011 air pollutant concentrations constant until 2050. We predicted the health and economic impacts from air pollution for the scenarios until 2050, and the inequalities in exposure across different socioeconomic groups. FINDINGS: NO2 concentrations declined leading to 4 892 000 life-years saved for the nuclear power scenario and 7 178 000 life-years saved for the low-greenhouse gas scenario from 2011 to 2154. However, the associations that we used might overestimate the effects of NO2 itself. PM2·5 concentrations in Great Britain are predicted to decrease between 42% and 44% by 2050 compared with 2011 in the scenarios that met the Climate Change Act targets, especially those from road traffic and off-road machinery. These reductions in PM2·5 are tempered by a 2035 peak (and subsequent decline) in biomass (wood burning), and by a large, projected increase in future demand for transport leading to potential increases in non-exhaust particulate matter emissions. The potential use of biomass in poorly controlled technologies to meet the Climate Change Act commitments would represent an important missed opportunity (resulting in 472 000 more life-years lost from PM2·5 in the low-greenhouse gas scenario and 1 122 000 more life-years lost in the nuclear power scenario from PM2·5 than the baseline scenario). Although substantial overall improvements in absolute amounts of exposure are seen compared with 2011, these outcomes mask the fact that health inequalities seen (in which socioeconomically disadvantaged populations are among the most exposed) are projected to be maintained up to 2050. INTERPRETATION: The modelling infrastructure created will help future researchers explore a wider range of climate policy scenarios, including local, European, and global scenarios. The need to strengthen the links between climate change policy objectives and public health imperatives, and the benefits to societal wellbeing that might result is urgent. FUNDING: National Institute for Health Research.

Are noise and air pollution related to the incidence of dementia? A cohort study in London, England.

OBJECTIVE: To investigate whether the incidence of dementia is related to residential levels of air and noise pollution in London. DESIGN: Retrospective cohort study using primary care data. SETTING: 75 Greater London practices. PARTICIPANTS: 130 978 adults aged 50-79 years registered with their general practices on 1 January 2005, with no recorded history of dementia or care home residence. PRIMARY AND SECONDARY OUTCOME MEASURES: A first recorded diagnosis of dementia and, where specified, subgroups of Alzheimer's disease and vascular dementia during 2005-2013. The average annual concentrations during 2004 of nitrogen dioxide (NO2), particulate matter with a median aerodynamic diameter ≤2.5 µm (PM2.5) and ozone (O3) were estimated at 20×20 m resolution from dispersion models. Traffic intensity, distance from major road and night-time noise levels (Lnight) were estimated at the postcode level. All exposure measures were linked anonymously to clinical data via residential postcode. HRs from Cox models were adjusted for age, sex, ethnicity, smoking and body mass index, with further adjustments explored for area deprivation and comorbidity. RESULTS: 2181 subjects (1.7%) received an incident diagnosis of dementia (39% mentioning Alzheimer's disease, 29% vascular dementia). There was a positive exposure response relationship between dementia and all measures of air pollution except O3, which was not readily explained by further adjustment. Adults living in areas with the highest fifth of NO2 concentration (>41.5 µg/m3) versus the lowest fifth (<31.9 µg/m3) were at a higher risk of dementia (HR=1.40, 95% CI 1.12 to 1.74). Increases in dementia risk were also observed with PM2.5, PM2.5 specifically from primary traffic sources only and Lnight, but only NO2 and PM2.5 remained statistically significant in multipollutant models. Associations were more consistent for Alzheimer's disease than vascular dementia. CONCLUSIONS: We have found evidence of a positive association between residential levels of air pollution across London and being diagnosed with dementia, which is unexplained by known confounding factors.