The impact of COVID-19 public health restrictions on particulate matter pollution measured by a validated low-cost sensor network in Oxford, UK.

Emergency responses to the COVID-19 pandemic led to major changes in travel behaviours and economic activities with arising impacts upon urban air quality. To date, these air quality changes associated with lockdown measures have typically been assessed using limited city-level regulatory monitoring data, however, low-cost air quality sensors provide capabilities to assess changes across multiple locations at higher spatial-temporal resolution, thereby generating insights relevant for future air quality interventions. The aim of this study was to utilise high-spatial resolution air quality information utilising data arising from a validated (using a random forest field calibration) network of 15 low-cost air quality sensors within Oxford, UK to monitor the impacts of multiple COVID-19 public heath restrictions upon particulate matter concentrations (PM10, PM2.5) from January 2020 to September 2021. Measurements of PM10 and PM2.5 particle size fractions both within and between site locations are compared to a pre-pandemic related public health restrictions baseline. While average peak concentrations of PM10 and PM2.5 were reduced by 9-10 µg/m3 below typical peak levels experienced in recent years, mean daily PM10 and PM2.5 concentrations were only ∼1 µg/m3 lower and there was marked temporal (as restrictions were added and removed) and spatial variability (across the 15-sensor network) in these observations. Across the 15-sensor network we observed a small local impact from traffic related emission sources upon particle concentrations near traffic-oriented sensors with higher average and peak concentrations as well as greater dynamic range, compared to more intermediate and background orientated sensor locations. The greater dynamic range in concentrations is indicative of exposure to more variable emission sources, such as road transport emissions. Our findings highlight the great potential for low-cost sensor technology to identify highly localised changes in pollutant concentrations as a consequence of changes in behaviour (in this case influenced by COVID-19 restrictions), generating insights into non-traffic contributions to PM emissions in this setting. It is evident that additional non-traffic related measures would be required in Oxford to reduce the PM10 and PM2.5 levels to within WHO health-based guidelines and to achieve compliance with PM2.5 targets developed under the Environment Act 2021.

Development of a semi-empirical physical model for transient NOx emissions prediction from a high-speed diesel engine.

With emissions regulations becoming increasingly restrictive and the advent of real driving emissions limits, control of engine-out NOx emissions remains an important research topic for diesel engines. Progress in experimental engine development and computational modelling has led to the generation of a large amount of high-fidelity emissions and in-cylinder data, making it attractive to use data-driven emissions prediction and control models. While pure data-driven methods have shown robustness in NOx prediction during steady-state engine operation, deficiencies are found under transient operation and at engine conditions far outside the training range. Therefore, physics-based, mean value models that capture cyclic-level changes in in-cylinder thermo-chemical properties appear as an attractive option for transient NOx emissions modelling. Previous experimental studies have highlighted the existence of a very strong correlation between peak cylinder pressure and cyclic NOx emissions. In this study, a cyclic peak pressure-based semi-empirical NOx prediction model is developed. The model is calibrated using high-speed NO and NO2 emissions measurements during transient engine operation and then tested under different transient operating conditions. The transient performance of the physical model is compared to that of a previously developed data-driven (artificial neural network) model, and is found to be superior, with a better dynamic response and low (<10%) errors. The results shown in this study are encouraging for the use of such models as virtual sensors for real-time emissions monitoring and as complimentary models for future physics-guided neural network development.

Impacts of daily household activities on indoor particulate and NO2 concentrations; a case study from oxford UK.

This study explores indoor air pollutant (PM1, PM2.5 and NO2) concentrations over a 15-week period during the COVID-19 pandemic in a typical suburban household in Oxford, UK. A multi-room intensive monitoring study was conducted in a single dwelling using 10 air quality sensors measuring real-time pollutant concentrations at 10 second intervals to assess temporal and spatial variability in PM1, PM2.5 and NO2 concentrations, identify pollution-prone areas, and investigate the impact of residents' activities on indoor air quality. Significant spatial variations in PM concentrations were observed within the study dwelling, with highest hourly concentrations (769.0 & 300.9 µg m-3 for PM2.5, and PM1, respectively) observed in the upstairs study room, which had poor ventilation. Cooking activities were identified as a major contributor to indoor particulate pollution, with peak concentrations aligning with cooking events. Indoor NO2 levels were typically higher than outdoor levels, particularly in the kitchen where a gas-cooking appliance was used. There was no significant association observed between outdoor and indoor PM concentrations; however, a clear correlation was evident between kitchen PM emissions and indoor levels. Similarly, outdoor NO2 had a limited influence on indoor air quality compared to kitchen activities. Indoor sources were found to dominate for both PM and NO2, with higher Indoor/Outdoor (I/O) ratios observed in the upstairs bedroom and the kitchen. Overall, our findings highlight the contribution of indoor air pollutant sources and domestic activities to indoor air pollution exposure, notably during the COVID-19 pandemic when people were typically spending more time in domestic settings. Our novel findings, which suggest high levels of pollutant concentrations in upstairs (first floor) rooms, underscore the necessity for targeted interventions. These interventions include the implementation of source control measures, effective ventilation strategies and occupant education for behaviour change, all aimed at improving indoor air quality and promoting healthier living environments.

Impacts of ambient air quality on acute asthma hospital admissions during the COVID-19 pandemic in Oxford City, UK: a time-series study.

OBJECTIVES: The study aims to investigate the short-term associations between exposure to ambient air pollution (nitrogen dioxide (NO2), particulate matter pollution-particles with diameter<2.5 µm (PM2.5) and PM10) 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 NO2, PM2.5 and PM10 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 (NO2, PM2.5, PM10) and multipollutant (NO2 and PM2.5, NO2 and PM10) 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/m3 increase in monthly concentrations of NO2 4% (95% CI 1.009% to 1.072%), PM2.5 3% (95% CI 1.006% to 1.052%) and PM10 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 NO2 and PM2.5 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.

On the pursuit of emissions-free clean mobility - Electric vehicles versus e-fuels.

With the passing of every second we get closer to a society that is more cognizant of the effect carbon dioxide emissions are having on our planet, and that is more willing to take part in sustainable efforts to combat this and ever more interested in investing in cleaner technologies like electric vehicles (EVs). EVs are marching strongly into a market that is currently dominated by internal combustion engine vehicles, the current main fuel of which is a known contributor to most of the emission related climate problems that we now find ourselves in. Moving ahead, it is important that any move from internal combustion engines to more nascent technologies like EVs is sustainable and not detrimental to the environment. There is an ongoing debate between proponents of so-called e-fuels (being synthetic fuels made from atmospheric carbon dioxide, water, and renewable energy) and EVs wherein e-fuels are largely accused of being a half-measure while EVs are thought to contribute more in terms of brake and tire emissions than the ICE vehicles. This raises the question of whether there should even be a complete replacement of the combustion engine vehicle fleet or that should there be a 'mobility mix' similar to how we currently refer to an energy mix with power grids. This article offers some perspectives by critically analyzing and diving deeper into these pressing concerns to answer some of these questions.

Impacts of emergency health protection measures upon air quality, traffic and public health: evidence from Oxford, UK.

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 NO2 concentrations of 38% (SD ± 24.0%) at roadside and 17% (SD ± 5.4%) at urban background locations. Observed changes in PM2.5, PM10 and O3 concentrations were not significant during first or second lockdown. Deweathering and detrending analyses revealed a 22% (SD ± 4.4%) reduction in roadside NO2 and 2% (SD ± 7.1%) at urban background with no significant changes in the second lockdown. Deweathered-detrended PM2.5 and O3 concentration changes were not significant, but PM10 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 NO2 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 NO2 emissions decreased during both lockdown periods, the overall contribution from buses increased relative to cars in the second lockdown. Sustained NO2 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.

An optical method for measuring exhaust gas pressure from an internal combustion engine at high speed.

Measurement of exhaust gas pressure at high speed in an engine is important for engine efficiency, computational fluid dynamics analysis, and turbocharger matching. Currently used piezoresistive sensors are bulky, require cooling, and have limited lifetimes. A new sensor system uses an interferometric technique to measure pressure by measuring the size of an optical cavity, which varies with pressure due to movement of a diaphragm. This pressure measurement system has been used in gas turbine engines where the temperatures and pressures have no significant transients but has never been applied to an internal combustion engine before, an environment where both temperature and pressure can change rapidly. This sensor has been compared with a piezoresistive sensor representing the current state-of-the-art at three engine operating points corresponding to both light load and full load. The results show that the new sensor can match the measurements from the piezoresistive sensor except when there are fast temperature swings, so the latter part of the pressure during exhaust blowdown is only tracked with an offset. A modified sensor designed to compensate for these temperature effects is also tested. The new sensor has shown significant potential as a compact, durable sensor, which does not require external cooling.