Detecting and quantifying the contribution made by aircraft emissions to ambient concentrations of nitrogen oxides in the vicinity of a large international airport

Plans to build a third runway at London Heathrow Airport (LHR) have been held back because of concerns that the development would lead to annual mean concentrations of nitrogen dioxide (NO2) in excess of EU Directives, which must be met by 2010. The dominant effect of other sources of NOX close to the airport, primarily from road traffic, makes it difficult to detect and quantify the contribution made by the airport to local NOX and NO2 concentrations. This work presents approaches that aim to detect and quantify the airport contribution to NOX at a network of seven measurement sites close to the airport. Two principal approaches are used. First, a graphical technique using bivariate polar plots that develops the idea of a pollution rose is used to help discriminate between different source types. The sampling uncertainties associated with the technique have been calculated through a randomised re-sampling approach. Second, the unique pattern of aircraft activity at LHR enables data filtering techniques to be used to statistically verify the presence of aircraft sources. It is shown that aircraft NOX sources can be detected to at least 2.7 km from the airport, despite that the airport contribution is very small at that distance. Using these approaches, estimates have been made of the airport contribution to long-term mean concentrations of NOX and NO2. At the airport boundary we estimate that approximately 28 % (34 μg m-3) of the annual mean NOX is due to airport operations. At background locations 2-3 km downwind of the airport we estimate that the upper limit of the airport contribution to be less than 15 % (< 10 μg m-3). This work also provides approaches that would help validate and refine dispersion modelling studies used for airport assessments

Air Pollution and Subtypes, Severity and Vulnerability to Ischemic Stroke—A Population Based Case-Crossover Study

Few studies have examined the association between air pollutants and ischemic stroke subtypes. We examined acute effects of outdoor air pollutants (PM10, NO2, O3, CO, SO2) on subtypes and severity of incident ischemic stroke and investigated if pre-existing risk factors increased susceptibility.We used a time stratified case-crossover study and stroke cases from the South London Stroke Register set up to capture all incident cases of first ever stroke occurring amongst residents in a geographically defined area. The Oxford clinical and TOAST etiological classifications were used to classify subtypes. A pragmatic clinical classification system was used to assess severity. Air pollution concentrations from the nearest background air pollution monitoring stations to patients' residential postcode centroids were used. Lags from 0 to 6 days were investigated.There were 2590 incident cases of ischemic stroke (1995-2006). While there were associations at various lag times with several pollutants, overall, there was no consistent pattern between exposure and risk of ischemic stroke subtypes or severity. The possible exception was the association between NO2 exposure and small vessel disease stroke-adjusted odds ratio of 1.51 (1.12-2.02) associated with an inter-quartile range increase in the lag 0-6 day average for NO2. There were no clear associations in relation to pre-existing risk factors.Overall, we found little consistent evidence of association between air pollutants and ischemic stroke subtypes and severity. There was however a suggestion that increasing NO2 exposure might be associated with higher risk of stroke caused by cerebrovascular small vessel disease

Implementing precision methods in personalizing psychological therapies: barriers and possible ways forward

This is the final version. Available on open access from Elsevier via the DOI in this recordData availability: No data was used for the research described in the article.Highlights: • Personalizing psychological treatments means to customize treatment for individuals to enhance outcomes. • The application of precision methods to clinical psychology has led to data-driven psychological therapies. • Applying data-informed psychological therapies involves clinical, technical, statistical, and contextual aspects

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 μm 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 μg/m3 (SD, 3.3; range, 32.2 to 103.2) for nitrogen dioxide and 25 μg/m3 (SD, 1.3; range, 22.7 to 52) for particulate matter <10 μm in diameter. A 10-μg/m3 increase in nitrogen dioxide was associated with a 28% (95% CI, 11% to 48%) increase in risk of death. A 10-μg/m3 increase in particulate matter <10 μm 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-μg/m3 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 strokePeer reviewe

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

Background: 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. &quot;True&quot; 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 &quot;true&quot; 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. © 2020 Wolters Kluwer Health. All rights reserved

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

Background: 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 &amp;lt;10 µm (PM10) and &amp;lt;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 &quot;true&quot; underlying daily exposure surfaces for PM10and PM2.5for 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 PM2.5for 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 PM10and 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. © 2020 Wolters Kluwer Health. All rights reserved