Air pollution is a worldwide environmental health issue. Increasingly, reports suggest that poor air quality may be associated with mental health problems, but these studies often use global measures and rarely focus on early development when psychopathology commonly emerges. To address this, we combined high-resolution air pollution exposure estimates and prospectively-collected phenotypic data to explore concurrent and longitudinal associations between air pollutants of major concern in urban areas and mental health problems in childhood and adolescence. Exploratory analyses were conducted on 284 London-based children from the Environmental Risk (E-Risk) Longitudinal Twin Study. Exposure to annualized PM2.5 and NO2 concentrations was estimated at address-level when children were aged 12. Symptoms of anxiety, depression, conduct disorder, and attention-deficit hyperactivity disorder were assessed at ages 12 and 18. Psychiatric diagnoses were ascertained from interviews with the participants at age 18. We found no associations between age-12 pollution exposure and concurrent mental health problems. However, age-12 pollution estimates were significantly associated with increased odds of major depressive disorder at age 18, even after controlling for common risk factors. This study demonstrates the potential utility of incorporating high-resolution pollution estimates into large epidemiological cohorts to robustly investigate associations between air pollution and youth mental health.

Transport-related air and noise pollution, exposures linked to adverse health outcomes, varies within cities potentially resulting in exposure inequalities. Relatively little is known regarding inequalities in personal exposure to air pollution or transport-related noise. Our objectives were to quantify socioeconomic and ethnic inequalities in London in 1) air pollution exposure at residence compared to personal exposure; and 2) transport-related noise at residence from different sources. We used individual-level data from the London Travel Demand Survey (n = 45,079) between 2006 and 2010. We modeled residential (CMAQ-urban) and personal (London Hybrid Exposure Model) particulate matter £75,000) had lower residential NO2 (-1.3 (95% CI -2.1, -0.6) μg/m3 in the 95th exposure quantile) but higher personal NO2 exposure (1.9 (95% CI 1.6, 2.3) μg/m3 in the 95th quantile), which was driven largely by transport mode and duration. Inequalities in residential exposure to NO2 with respect to area-level deprivation were larger at lower exposure quantiles (e.g. estimate for NO2 5.1 (95% CI 4.6, 5.5) at quantile 0.15 versus 1.9 (95% CI 1.1, 2.6) at quantile 0.95), reflecting low-deprivation, high residential NO2 areas in the city centre. Air pollution exposure at residence consistently overestimated personal exposure; this overestimation varied with age, household income, and area-level income deprivation. Inequalities in road traffic noise were generally small. In logistic regression models, the odds of living within a 50 dB contour of aircraft noise were highest in individuals with the highest household income, white ethnicity, and with the lowest area-level income deprivation. Odds of living within a 50 dB contour of rail noise were 19% (95% CI 3, 37) higher for black compared to white individuals. Socioeconomic inequalities in air pollution exposure were different for modeled residential versus personal exposure, which has important implications for environmental justice and confounding in epidemiology studies. Exposure misclassification was dependent on several factors related to health, a potential source of bias in epidemiological studies. Quantile regression revealed that socioeconomic and ethnic inequalities in air pollution are often not uniform across the exposure distribution.

This research quantifies current sources of non-exhaust particulate matter traffic emissions in London using simultaneous, highly time-resolved, atmospheric particulate matter mass and chemical composition measurements. The measurement campaign ran at Marylebone Road (roadside) and Honor Oak Park (background) urban monitoring sites over a 12-month period between 1 September 2019 and 31 August 2020. The measurement data were used to determine the traffic increment (roadside–background) and covered a range of meteorological conditions, seasons, and driving styles, as well as the influence of the COVID-19 “lockdown” on non-exhaust concentrations. Non-exhaust particulate matter (PM)₁₀ concentrations were calculated using chemical tracer scaling factors for brake wear (barium), tyre wear (zinc), and resuspension (silicon) and as average vehicle fleet non-exhaust emission factors, using a CO₂ “dilution approach”. The effect of lockdown, which saw a 32% reduction in traffic volume and a 15% increase in average speed on Marylebone Road, resulted in lower PM₁₀ and PM_2.5 traffic increments and brake wear concentrations but similar tyre and resuspension concentrations, confirming that factors that determine non-exhaust emissions are complex. Brake wear was found to be the highest average non-exhaust emission source. In addition, results indicate that non-exhaust emission factors were dependent upon speed and road surface wetness conditions. Further statistical analysis incorporating a wider variability in vehicle mix, speeds, and meteorological conditions, as well as advanced source apportionment of the PM measurement data, were undertaken to enhance our understanding of these important vehicle sources.

Land use regression (LUR) and dispersion/chemical transport models (D/CTMs) are frequently applied to predict exposure to air pollution concentrations at a fine scale for use in epidemiological studies. Moreover, the use of satellite aerosol optical depth data has been a key predictor especially for particulate matter pollution and when studying large populations. Within the STEAM project we present a hybrid spatio-temporal modeling framework by (a) incorporating predictions from dispersion modeling of nitrogen dioxide (NO₂), ozone (O₃) and particulate matter with an aerodynamic diameter equal or less than 10 μm (PM10) and less than 2.5 μm (PM2.5) into a spatio-temporal LUR model; and (b) combining the predictions LUR and dispersion modeling and additionally, only for PM2.5, from an ensemble machine learning approach using a generalized additive model (GAM). We used air pollution measurements from 2009 to 2013 from 62 fixed monitoring sites for O3, 115 for particles and up to 130 for NO₂, obtained from the dense network in the Greater London Area, UK. We assessed all models following a 10-fold cross validation (10-fold CV) procedure. The hybrid models performed better compared to separate LUR models. Incorporation of the dispersion estimates in the LUR models as a predictor, improved the LUR model fit: CV-R² increased to 0.76 from 0.71 for NO₂, to 0.79 from 0.57 for PM10, to 0.81 to 0.66 for PM2.5 and to 0.75 from 0.62 for O₃. The CV-R² obtained from the hybrid GAM framework was also increased compared to separate LUR models (CV-R² = 0.80 for NO₂, 0.76 for PM10, 0.79 for PM2.5 and 0.75 for O₃). Our study supports the combined use of different air pollution exposure assessment methods in a single modeling framework to improve the accuracy of spatio-temporal predictions for subsequent use in epidemiological studies.

The burden of diseases attributable to air pollution is comparable to those of global health risks such as unhealthy diets and tobacco smoking, with many air pollution sources also emitting climate heating gases. In this UK study we estimated the co-benefits of Net Zero (NZ) climate policy on the health benefits of air pollution reduction, increased active travel, outdoor exposure inequalities and indoor air pollution changes. The study focused on two of the largest UK sources, road transport and building heating, with comparisons made between NZ and UK existing policy, referred to as Business as Usual (BAU). Particulate matter (PM_2.5), Nitrogen Dioxide (NO₂) and Ozone (O₃) projections were made between 2019 and 2050, with emphasis placed upon the NZ co-benefits in 2030 and 2040. We compared the UK BAU scenarios with the Climate Change Committee's (CCC) Balanced Net Zero Pathway (BNZP) and Widespread Innovation (WI) pathway. Compared to BAU predictions, BNZP assumptions lead to more electric vehicles, reduced vehicle km, more low carbon building heating, and reduced emissions of NO₂ and PM_2.5. By 2040 under BNZP, relative to BAU, the buildings sector was predicted to be three times more effective at reducing PM_2.5 than road transport. To help reduce the inequality gap the NZ building transition was tailored toward those most in need. Outdoor air pollution exposure inequalities prevailed across the socioeconomic spectrum, especially for NO₂, but were less pronounced due in part to NZ policies. Core air quality health benefits for the BNZP buildings sector were £21.3 billion (16.4 to 26.2) by 2050 and £98.4 billion (75.7 to 121.1) by 2154. For the transport sector the health benefits were £9.1 billion (7.0 to 11.2) by 2050 and £36.5 billion (28.1 to 44.9) by 2154. NZ building sector operating costs did not achieve break-even via efficiency savings, but with Greenhouse Gas (GHG) (lower benefits) break-even was achieved in 2052. With additional air pollution health benefits, building-sector time to break-even improved by between 3.1 (2.5 to 4.7) and 6.3 (4.7 to 7.6) years to between 2046 and 2049. Analysis found that removing gas cooking at home, for NZ, may result in greater concentration reductions than outdoor air pollution for NO₂. Net Zero health and economic co-benefits are large, as are the changes needed, requiring political leadership and public engagement. Our findings are relevant to other countries facing the NZ transition.