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In a drive to achieve net zero emissions, U.K. transport decarbonisation policies are predominantly focussed on measures to promote the uptake and use of electric vehicles (EVs). This is reflected in the COP26 Transport Declaration signed by 38 national governments, alongside city region governments, vehicle manufacturers and investors. However, emerging evidence suggests that EVs present multiple challenges for air quality, mobility and health, including risks from non-exhaust emissions (NEEs) and increasing reliance on vehicles for short trips. Understanding the interconnected links between electric mobility, human health and the environment, including synergies and trade-offs, requires a whole systems approach to transport policymaking. In the present paper, we describe the use of Participatory Systems Mapping (PSM) in which a diverse group of stakeholders collaboratively constructed a causal model of the U.K. surface transport system through a series of interactive online workshops. We present the map and its analysis, with our findings illustrating how unintended consequences of EV-focussed transport policies may have an impact on air quality, human health and important social functions of the transport system. We conclude by considering how online participatory causal modelling techniques could be effectively integrated with empirical metrics to facilitate effective policy design and appraisal in the transport sector. [ABSTRACT FROM AUTHOR]

Background: Tobacco control policies have potential to be an effective strategy for the reduction of smoking prevalence and secondhand smoke (SHS) exposure in tertiary educational settings worldwide. The aims of this study were to collect baseline data among staff and students, to measure smoking behaviours and attitudes towards introduction of campus-wide tobacco control policies within a UK higher education setting.Methods: Cross-sectional study using data collected by web-based questionnaire administered to employed staff and enrolled students (undergraduate/postgraduate) at the University of Birmingham from May 2016 to April 2017. Information was obtained regarding demographic characteristics, tobacco usage patterns and attitudes towards a revised campus tobacco control policy using a 21-item survey tool. Logistic regression analyses were used to explore associations between participant characteristics and support for smoke-free or tobacco-free campus policy options, evaluated by crude and adjusted Odds Radios (OR) after controlling for confounding factors (significance level: P < 0.05).Results: A total of 934 survey responses were received, of whom 780 participants provided complete information on staff or student status and were included in the present analysis. Current smoking prevalence was 14% (N = 109; 95% confidence interval (CI) 11.6-16.6). Overall, 66.3% (95% CI: 62.9-69.7) of participants supported a smoke-free campus; 68.5% (95% CI: 65.2-71.8) endorsed restrictions for tobacco sales and just under half of respondents (47.3%; 95% CI: 43.8-50.9) supported a ban for electronic cigarettes/vaping device use on campus. Smoking status was an independent predictor of support for tobacco control, with the lowest level of support for a smoke-free campus among daily (adjusted OR 0.02; 95% CI: 0.01-0.05) and intermittent smokers (adjusted OR 0.06; 95% CI: 0.02-0.16).Conclusions: Overall, the majority of staff and students participating in this baseline survey supported implementation of a smoke-free or comprehensive tobacco-free campus policy. These findings may inform the development and future implementation of a revised tobacco control policy at the university which reflects contemporary attitudes and considers a broad range of implementation issues, including behaviour change and environmental adaptations. [ABSTRACT FROM AUTHOR]

Objectives: The study aims to investigate the short-term associations between exposure to ambient air pollution (nitrogen dioxide (NO 2 ), particulate matter pollution-particles with diameter<2.5 µm (PM 2.5 ) and PM 10 ) and incidence of asthma hospital admissions among adults, in Oxford, UK., Design: Retrospective time-series study., Setting: Oxford City (postcode areas OX1-OX4), UK., Participants: Adult population living within the postcode areas OX1-OX4 in Oxford, UK from 1 January 2015 to 31 December 2021., Primary and Secondary Outcome Measures: Hourly NO 2 , PM 2.5 and PM 10 concentrations and meteorological data for the period 1 January 2015 to 31 December 2020 were analysed and used as exposures. We used Poisson linear regression analysis to identify independent associations between air pollutant concentrations and asthma admissions rate among the adult study population, using both single (NO 2 , PM 2.5 , PM 10 ) and multipollutant (NO 2 and PM 2.5 , NO 2 and PM 10 ) models, where they adjustment for temperature and relative humidity., Results: The overall 5-year average asthma admissions rate was 78 per 100 000 population during the study period. The annual average rate decreased to 46 per 100 000 population during 2020 (incidence rate ratio 0.58, 95% CI 0.42 to 0.81, p<0.001) compared to the prepandemic years (2015-2019). In single-pollutant analysis, we observed a significantly increased risk of asthma admission associated with each 1 μg/m 3 increase in monthly concentrations of NO 2 4% (95% CI 1.009% to 1.072%), PM 2.5 3% (95% CI 1.006% to 1.052%) and PM 10 1.8% (95% CI 0.999% to 1.038%). However, in the multipollutant regression model, the effect of each individual pollutant was attenuated., Conclusions: Ambient NO 2 and PM 2.5 air pollution exposure increased the risk of asthma admissions in this urban setting. Improvements in air quality during COVID-19 lockdown periods may have contributed to a substantially reduced acute asthma disease burden. Large-scale measures to improve air quality have potential to protect vulnerable people living with chronic asthma in urban areas., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY. Published by BMJ.)

Emergency responses to the COVID-19 pandemic led to major changes in travel behaviours and economic activities in 2020. Machine learning provides a reliable approach for assessing the contribution of these changes to air quality. This study investigates impacts of health protection measures upon air pollution and traffic emissions and estimates health and economic impacts arising from these changes during two national 'lockdown' periods in Oxford, UK. Air quality improvements were most marked during the first lockdown with reductions in observed NO 2 concentrations of 38% (SD ± 24.0%) at roadside and 17% (SD ± 5.4%) at urban background locations. Observed changes in PM 2.5, PM 10 and O 3 concentrations were not significant during first or second lockdown. Deweathering and detrending analyses revealed a 22% (SD ± 4.4%) reduction in roadside NO 2 and 2% (SD ± 7.1%) at urban background with no significant changes in the second lockdown. Deweathered-detrended PM 2.5 and O 3 concentration changes were not significant, but PM 10 increased in the second lockdown only. City centre traffic volume reduced by 69% and 38% in the first and second lockdown periods. Buses and passenger cars were the major contributors to NO 2 emissions, with relative reductions of 56% and 77% respectively during the first lockdown, and less pronounced changes in the second lockdown. While car and bus NO 2 emissions decreased during both lockdown periods, the overall contribution from buses increased relative to cars in the second lockdown. Sustained NO 2 emissions reduction consistent with the first lockdown could prevent 48 lost life-years among the city population, with economic benefits of up to £2.5 million. Our findings highlight the critical importance of decoupling emissions changes from meteorological influences to avoid overestimation of lockdown impacts and indicate targeted emissions control measures will be the most effective strategy for achieving air quality and public health benefits in this setting., (Copyright © 2021 The Authors. Published by Elsevier Ltd.. All rights reserved.)