Air pollution arising from hydrogen combustion

Expanding the use of hydrogen as a zero carbon fuel has some potential implications for air quality if it is used as a combustion fuel. Using hydrogen to power fuel cells that directly generate electricity does not create any air pollution at point-of-use. When hydrogen is burned in engines, boilers, cookers and furnaces the very high temperature of the flames can split apart nitrogen (N2) in the air leading to the formation of nitrogen oxides (NOx), animportant class of air pollutant. Hydrogen burns with a hotter flame than most fossil fuels and has the potential to emit more NOx per unit of heat that is generated. On the other hand burning hydrogen gas instead of hydrocarbon fuels (e.g. gasoline, diesel, or bioderived fuels such as ethanol) can bring benefits for air quality, producing lower emissions of particulatematter and eliminating carbon monoxide. Reducing emissions of NOx from the combustion of hydrogen can be achieved in many situations using existing exhaust after treatment technologies, by lowering the temperature at which the hydrogen is burned and by optimising the ratio of fuel to air. This can sometimes leads to additional cost and/or a reduction in energy efficiency. There are few commercially available examples of engines orboilers that have been specifically designed to burn hydrogen and real-world data on emissions performance is very limited. To ensure hydrogen fulfils its potential to be a substantially cleaner fuel from an air quality perspective requires effective NOx emission controls (both technical and regulatory) to be in place

Statement on integration and adoption of AI in air quality

The field of Artificial Intelligence (AI) has evolved significantly over recent years, with increasing availability of underlying methodologies and adoption across academia, the public and private sectors. This coincides with increased public discourse around AI and the potential opportunities and potential dangers posed, partly driven by the public release of facilities such as ChatGPT and the creation of national and international forums around regulation and safety.In parallel, increasing diversity in air quality sampling and simulation technologies, combined with the need for integration with complex health and socio-economic outcomes, positions air quality science and policy as a potential user of AI tools. With this in mind AQEG considers the utility of Artificial Intelligence (AI) and associated innovation landscape whilst considering risks and sustainable mechanisms for use. The report differentiates between Artificial Intelligence (AI) as a goal of autonomous intelligence, Machine Learning (ML) as a subset of AI that concerns development of algorithms to extract meaning from data and Data Science as the broader umbrella that encapsulates AI, ML and includes consideration of regulation, standards and ethics. Withinthe remit of this report and at the time of writing, the majority of air quality exemplars use machine learning (ML). Evidenced benefits of ML for air quality include development of new methods to infer contributions from different emissions to measured instrument response, to development of causal inference of policy change. In addition, service-driven industries are rapidly adopting ML tools within their data pipelining, including air quality instruments. Data driven models offer a route to mitigate traditional challenges of computational resource barriers in regional to global air quality models. AQEG foresee use of time-series forecasting from single sites or networks through to development of personal exposure estimates built around mobility data. It is widely accepted that the pathway to increased model resolution and integration of increasing amounts of data can only be met through inclusion of machine learning approaches. National research centres and laboratories are already reframing existing models with this in mind.Despite the dominant current use of isolated ML in air quality there is significant potential offered by AI. This includes the use of Digital Twins in automated systems management and the application of Foundational Models. A Digital Twin might use near real-time data to change the state of the system being studied in advance of a desired outcome. This feedback mechanism separates a Digital Twin from a Digital Shadow, or Digital Model (as considered in a traditional modelling sense); digital twins may require support communications infrastructure, cyber security and so on. Foundational Models have rapidly entered the public domain following the public release of the ChatGPT variants by OpenAI, with parallel responses by other vendors including Google, Meta and IBM. These large models, trained on vast quantities of unlabelled data, replace task specific models and are able to adapt to more generic use cases. Geospatial Foundational Models are now used to understand the impacts of extreme weather events such as predicting the extent of flooding and forest fires. It is likely only a matter of time before they are applied to global air pollution.This may change the balance between services offered and maintained between the public, private and academic sectors.Quantifying the success of AI adoption requires first for a clear strategy to be defined. This should identify where existing operations might benefit from potential use of AI technologies and create a value proposition with a range of stakeholders. This co-design with stakeholders could benefit government departments for a number of reasons including building trust, maintaining sustainable partnerships and positioning government at the forefront of discussions around standards and regulations. A strategy around adoption and use should also clarify a governance structure which goes beyond awareness of the IT or digital tools to definitions of roles and responsibilities on staff, operations and relationships with AI technologies. The creation of an advisory board, for example, for cross sector partnerships around adoption of AI could be an effective vehicle to maintain an appreciationof the breadth of activity. Membership could include representations from cross government departments, academia and industry.Whilst regulation of AI is beyond the responsibility of a single organisation, there is strength in forming such partnerships across government departments, industry and academia. This is particularly important where the state-of-the-art can change in a short space of time along with calls for wider consensus around both regulation and standards. Partnerships with academia could usefully vary from individual secondments, joint PhDs through to co-funded programmes of development across e.g. UKRI. The benefit of such arrangements includes increased external problem visibility and the opportunity for knowledge transfer aroundsuccessful demonstrations of technologies and work practices. AQEG recognise the importance of engaging with the AI industry which is likely to act as a significant source of technical solutions at the environment-human health interface.Defra and Devolved Administrations should support staff to develop the necessary skills to be aware of, use and understand AI-driven technologies for air quality. Improved training could include joint programmes with HE institutions through to tailored training options provided by industry. Nurturing AI innovation for air quality science and policy, through partnerships with external organisations, would support a longer-term goal of attracting and retaining staff with AI skill sets. As with fluid movements around regulation and standards, retaining staff in the public sector is a much bigger challenge that would benefit from acollective vision across public sector organisations and academia.With all that in mind, the graduate workforce is likely to embrace data science as a core tool in the future. This will inevitably reduce the burden on organisations investing in targeted training. However, the need to provide an innovative and nurturing ‘AI aware’ environment will remain, with proposed activities and initiatives given in this report designed to facilitatethis. By considering the issues raised in this report, we would support Defra and the DAs in their commitments to exploring the exciting opportunities offered by AI in managing and improving air quality whilst retaining and building public trust in the policy decisions that may emerge

New Opportunities for Particulate Measurements

Airborne particulate measurement science is a substantial area of both academic andcommercial research with new tools and techniques emerging all the time. It is necessarytherefore to regularly reassess where new measurement techniques might be able toprovide additional insight to support national and local management of PM in the UK. Thisreport is not an exhaustive list of available techniques, but instead provides somedescriptions of key methods and new opportunities that may be of relevance to local andnational government. It is focused on proven techniques that are available for operationaluse in air quality and emissions measurement and does not cover emerging technologiesthat are currently only used within the research community.Most measurements currently commissioned by Defra, Devolved Administrations and localauthorities are associated with the measurement of the mass concentration of particlessmaller than 2.5 micrometres diameter (PM2.5) and 10 micrometres (PM10). These extensive(and growing) networks of monitors provide the evidence for attainment of ambient airquality standards. These will continue to be needed in the coming decades and will providethe primary indicator of progress towards targets set in the Environment Act (2021). Thechallenges of measuring PM2.5 in a consistent and reproducible manner, and atconcentrations below the recently updated target annual average limit value of 10 g m-3should not be underestimated; continued investment in monitoring, calibration andperformance evaluation are essential.Whilst PM2.5 will continue to be the primary health–relevant air quality metric that ismeasured in the UK, evidence has strengthened around the role played by smaller particlesin causing harm (sub-micrometre and nanometre scale) . Further expansion of long-termurban measurements of particle number and ultrafine particles, and more broadly the sizedistributions of particles in UK air, would provide an opportunity to understand more abouttheir sources and to support future epidemiological research. The inclusion of particlenumber and ultrafine particles in tailpipe emission standards for transport sources (e.g. inEuro 6 and proposed Euro 7 standards for road vehicles and proposed internationalstandards for aviation) will further increase the necessity for ambient monitoring.Measurement of the chemical composition of particles continues to be an area of intensiveacademic study, but it has been used to only a limited extent by policymakers andregulators. Growth in technological capabilities, hardware, software and data analytics, havecreated opportunities for more detailed information of this kind to inform on individual PMsources and their changes over time. Such information can provide insight into the progressof individual sectors in managing emissions. Such measurements can assess theeffectiveness of interventions to reduce primary PM and precursor emissions of secondaryPM from sources including woodburning, agriculture and road transport. The composition ofPM also provides rich information that can be used to test and improve the performance ofmodels, enabling better short-term forecasts of pollution and models used for assessmentsof the impacts of future emission scenarios. More detailed knowledge of the types of PMemitted from individual sources, such as from tyre and brake wear, from the tailpipe, fromindustry and so on, can enhance the accuracy of emissions inventories, improveinternational reporting and in turn improve model performance.Whilst the vast majority of existing PM monitoring is conducted long-term and in situ at fixedmeasurement sites, a range of other techniques are available that can give additional insightinto source strengths and the spatial distribution of pollution. Compact sensors provide anopportunity to expand the number of indicative PM monitors within a community, includingsensors carried by people, used by community groups, and sensors designed for indoorspaces. Earth observation satellites can now provide a national-scale picture of the spatialdistribution of PM across the UK. These alternatives to fixed monitoring cabinets may helpidentify previously unidentified sources, find hotspots and support decision-making duringunplanned events such as accidents or wildfires. These can provide significantcomplementary information that adds value to existing in situ measurements from networkssuch as the AURN. There are further opportunities to use shorter more focused periods ofintensive research measurements to provide evaluation of models and emissions estimates,and to test process knowledge of the PM lifecycle.The detailed information on the nature of PM pollution that can be derived from sometechniques provides an opportunity for enhanced communication and information systemsfor the public. Realtime (or near real-time) information on PM pollution and its contributorysources during poor air quality events may help support behavioural adaptations andevidence the need for interventions and action. The co-location of multiple analyticaltechniques in one location, disaggregating PM into its many sub-components is a nowproven powerful approach that delivers a scientific and evidential value that is greater thanthe sum of the parts. PM measurements should be viewed by government as a key enablerfor effective air quality management, as well as a means for demonstrating regulatorycompliance.As measurement science advances it has been possible to gain insight into previouslyundetected sub-types of PM. For example, in recent years it has been demonstrated inresearch studies that it is possible to directly observe the presence of individual metals andthe microplastic components within respirable PM. It is now possible to routinely identifydifferent types of airborne bioaerosols in real-time. Offline laboratory techniques are givingever increasing insight into the molecular and biological composition of PM, broadening theboundaries of what should be considered of in the context of air quality and health. Foremerging classes of PM, particularly those involving microplastics and bioaerosols, there isa pressing need for investment in the development of standardised methodologies andreference materials to support future national scale monitoring. This is a key step in bridgingbetween short-term research studies and long-term measurements. The field of PMmeasurement has also benefited from continual advancements in remote sensing, inparticular observations from the latest generations of satellite instruments and their dataprocessing methods. When combined with models of the atmosphere and measurementsmade at the surface, these new data products offer opportunities to study the spatial extentof atmospheric PM and track plumes from large pollution events.Emission inventories report annual mass emissions of PM from different sources and includeinformation on different particle sizes (PM10, PM2.5) using emission rates measured at sourceor activity-based emission factors (mass emissions per unit of activity). A comparison ofemissions from different sources indicated by inventories can be hampered by the differentmeasurement techniques that are used, e.g. for combustion point sources, residentialcombustion, transport sources. Different measurement techniques and approaches do notalways determine equivalent PM (sometimes for technical or standardisation reasons) andmay not necessarily represent emissions in real-world conditions. Consequently, reportedemissions for some sectors may not be equivalent to emissions reported for other sectorsand may further differ from the definition of PM measured in ambient air.There are established measurement techniques for large industry sources but their suitabilityto cope with ever-reducing emission concentrations is becoming an issue both for periodicsampling methods and monitoring systems used for compliance assessment. There maytherefore be a requirement for renewed investment in new methods of detection to supportindustrial PM emissions control. The measurement of real-world exhaust emissions of PMfrom the latest generation of road vehicles is also becoming increasingly challenging asemissions have reduced to meet tighter regulations. The quantification of non-exhaust PMfrom tyre and brake wear at source and under real-world conditions remains a particularchallenge due to high variability and the need to establish a realistic testprocedure. Emission limits and test procedures for brake wear emissions have recentlybeen proposed within the Euro 7 regulations, recognising the increasingly importantcontribution these sources make to emissions and local PM concentrations in urban areas.As PM datasets become more extensive and complex the role played by data infrastructureand data methods grows. Extracting maximum value from investment in measurementsrequires sustained efforts to support innovation in use of data techniques alongsideresources for interpretation. The opportunities however are very significant. An expandedrole and scope for PM monitoring in the UK is likely to increase the evidence available todemonstrate the effectiveness of air pollution policy and technical interventions, providemore dynamic opportunities to use measurements to actively control pollution and increasepublic engagement with air quality more generally

Air pollution horizon-scanning:Seven potential risks of relevance to the UK

Horizon scanning is used to help identify potentially significant societal, economic or technological shifts which if they occurred would have major impacts on society. AQEG generally approaches the science and technology of air pollution either through retrospective analyses – what has happened to air quality and why, - or via future projections. These future projections are generally short to medium term and bounded by well-established science, but it is alsoAQEG’s role to identify evidence gaps that include uncertainties. It is valuable to periodically look beyond established evidence, towards emerging science to identify potential perturbations and assess risks that might plausibly lead to unexpected and large future air quality changes, for example those arising from climatological, technological and behavioural shifts. Since atmospheric chemistry is often non-linear in the generation of secondary pollutants and has dependencies on weather and climate, there exists the potential also for chemical and physical tipping points that may amplify changes in air quality (either positively or negatively). Often unanticipated air quality outcomes occur not because of a single large event but instead through the accumulation or interaction of multiple smaller changes.Air quality outcomes are closely linked to policy and regulation but also to hard-to-predict public choices around transport, diet and lifestyle. A possible impact from these types of future changes can be difficult to capture and often requires in-depth knowledge of the science field.Also noteworthy is that the chemical nature of air pollution is not fixed; it changes over time as sources change reflecting wider regulatory, technological and social trends. New perspectives can also arise from new scientific knowledge. The history of air pollution science is littered with events and discoveries that revealed new risks and required rapid evolution of regulation and policy. Examples include the great smog of London in 1952 and the Clean Air Act of 1956, the discovery of the pervasive harm from lead additives in fuel and the measurement campaigns of the 1970s that revealed that photochemical ozone was not just confined to warmer climates but affected air quality in western Europe too. On the health front research from the 1990’s revealed that the health-harm from long-term exposure was far greater than that from short-term smog events laying the foundations for modern air quality regulation. AtAQEG meeting 66 a round-table discussion on the long-term future for air quality in the UK was undertaken. Members each highlighted up to three areas of possibly under- recognised significance in a horizon scanning context. The focus of the discussion was on events, changes and processes that required specialist knowledge of the air pollution science field to discern rather than more generalised high impact and extreme events on air quality such as war and terrorism, chemical, biological, radiological or nuclear releases (CBRN) or major chemical accidents. These latter types of events are already identified in Defra Futures Team horizon scanning activities and more broadly are well-captured in the Cabinet Office National Risk Register. A wide range of issues related to atmospheric emissions, novel materials, human behaviours, monitoring, regulation, atmospheric processes and social factors were discussed. Anumber of consensus themes emerged which are summarised in this short note. 2 It is important to stress that the workshop did not explore the probability or likelihood of individual and/or cumulative outcomes occurring, only that the events or changes to processes were plausible based on current scientific understanding and that if actualised they could lead to large and currently unanticipated impacts on air quality. The existence of a scenario should not be interpreted as meaning it is likely to occur, and the existence of related risk is not a criticism of current technologies, regulations or policies in the relevant sectors. The intended audience for this paper is horizon scanning professionals within Defra, Government Office for Science and related Departments that have responsibilities for sectoral atmospheric emissions. The paper is made accessible publicly since it may be of wider interest and in line with AQEG principles of open and transparent communication of its work. Seven key horizon scan air pollution risks were (in no particular order): • Systemic underperformance of technical and regulatory air pollution abatement. • Multi-causal increases in atmospheric ammonia over the UK. • Increasing concentrations and health impacts of ultrafine particles (UFP). • Emergence of novel airborne materials and health effects • Climate-driven drought effects and increasing PM pollution. • Enhanced emissions of biological particles and antimicrobial resistance (AMR) • Loss of confidence in air pollution science and increasing uncertainty in forecastin

Mortality Among Adults With Cancer Undergoing Chemotherapy or Immunotherapy and Infected With COVID-19

Importance: Large cohorts of patients with active cancers and COVID-19 infection are needed to provide evidence of the association of recent cancer treatment and cancer type with COVID-19 mortality. // Objective: To evaluate whether systemic anticancer treatments (SACTs), tumor subtypes, patient demographic characteristics (age and sex), and comorbidities are associated with COVID-19 mortality. // Design, Setting, and Participants: The UK Coronavirus Cancer Monitoring Project (UKCCMP) is a prospective cohort study conducted at 69 UK cancer hospitals among adult patients (≥18 years) with an active cancer and a clinical diagnosis of COVID-19. Patients registered from March 18 to August 1, 2020, were included in this analysis. // Exposures: SACT, tumor subtype, patient demographic characteristics (eg, age, sex, body mass index, race and ethnicity, smoking history), and comorbidities were investigated. // Main Outcomes and Measures: The primary end point was all-cause mortality within the primary hospitalization. // Results: Overall, 2515 of 2786 patients registered during the study period were included; 1464 (58%) were men; and the median (IQR) age was 72 (62-80) years. The mortality rate was 38% (966 patients). The data suggest an association between higher mortality in patients with hematological malignant neoplasms irrespective of recent SACT, particularly in those with acute leukemias or myelodysplastic syndrome (OR, 2.16; 95% CI, 1.30-3.60) and myeloma or plasmacytoma (OR, 1.53; 95% CI, 1.04-2.26). Lung cancer was also significantly associated with higher COVID-19–related mortality (OR, 1.58; 95% CI, 1.11-2.25). No association between higher mortality and receiving chemotherapy in the 4 weeks before COVID-19 diagnosis was observed after correcting for the crucial confounders of age, sex, and comorbidities. An association between lower mortality and receiving immunotherapy in the 4 weeks before COVID-19 diagnosis was observed (immunotherapy vs no cancer therapy: OR, 0.52; 95% CI, 0.31-0.86). // Conclusions and Relevance: The findings of this study of patients with active cancer suggest that recent SACT is not associated with inferior outcomes from COVID-19 infection. This has relevance for the care of patients with cancer requiring treatment, particularly in countries experiencing an increase in COVID-19 case numbers. Important differences in outcomes among patients with hematological and lung cancers were observed

Dimethyl fumarate in patients admitted to hospital with COVID-19 (RECOVERY): a randomised, controlled, open-label, platform trial

Dimethyl fumarate (DMF) inhibits inflammasome-mediated inflammation and has been proposed as a treatment for patients hospitalised with COVID-19. This randomised, controlled, open-label platform trial (Randomised Evaluation of COVID-19 Therapy [RECOVERY]), is assessing multiple treatments in patients hospitalised for COVID-19 (NCT04381936, ISRCTN50189673). In this assessment of DMF performed at 27 UK hospitals, adults were randomly allocated (1:1) to either usual standard of care alone or usual standard of care plus DMF. The primary outcome was clinical status on day 5 measured on a seven-point ordinal scale. Secondary outcomes were time to sustained improvement in clinical status, time to discharge, day 5 peripheral blood oxygenation, day 5 C-reactive protein, and improvement in day 10 clinical status. Between 2 March 2021 and 18 November 2021, 713 patients were enroled in the DMF evaluation, of whom 356 were randomly allocated to receive usual care plus DMF, and 357 to usual care alone. 95% of patients received corticosteroids as part of routine care. There was no evidence of a beneficial effect of DMF on clinical status at day 5 (common odds ratio of unfavourable outcome 1.12; 95% CI 0.86-1.47; p = 0.40). There was no significant effect of DMF on any secondary outcome

Dimethyl fumarate in patients admitted to hospital with COVID-19 (RECOVERY): a randomised, controlled, open-label, platform trial

Dimethyl fumarate (DMF) inhibits inflammasome-mediated inflammation and has been proposed as a treatment for patients hospitalised with COVID-19. This randomised, controlled, open-label platform trial (Randomised Evaluation of COVID-19 Therapy [RECOVERY]), is assessing multiple treatments in patients hospitalised for COVID-19 (NCT04381936, ISRCTN50189673). In this assessment of DMF performed at 27 UK hospitals, adults were randomly allocated (1:1) to either usual standard of care alone or usual standard of care plus DMF. The primary outcome was clinical status on day 5 measured on a seven-point ordinal scale. Secondary outcomes were time to sustained improvement in clinical status, time to discharge, day 5 peripheral blood oxygenation, day 5 C-reactive protein, and improvement in day 10 clinical status. Between 2 March 2021 and 18 November 2021, 713 patients were enroled in the DMF evaluation, of whom 356 were randomly allocated to receive usual care plus DMF, and 357 to usual care alone. 95% of patients received corticosteroids as part of routine care. There was no evidence of a beneficial effect of DMF on clinical status at day 5 (common odds ratio of unfavourable outcome 1.12; 95% CI 0.86-1.47; p = 0.40). There was no significant effect of DMF on any secondary outcome

Healthcare Utilization and End-of-Life Outcomes in Patients Receiving CAR T-Cell Therapy

Background: CAR T-cell therapy has revolutionized the treatment of patients with hematologic malignancies, but it can result in prolonged hospitalizations and serious toxicities. However, data on the impact of CAR T-cell therapy on healthcare utilization and end-of-life (EoL) outcomes are lacking. Methods: We conducted a retrospective analysis of 236 patients who received CAR T-cell therapy at 2 tertiary care centers from February 2016 through December 2019. We abstracted healthcare utilization and EoL outcomes from the electronic health record, including hospitalizations, receipt of ICU care, hospitalization and receipt of systemic therapy in the last 30 days of life, palliative care, and hospice referrals. Results: Most patients (81.4%; n=192) received axicabtagene ciloleucel. Overall, 28.1% of patients experienced a hospital readmission and 15.5% required admission to the ICU within 3 months of CAR T-cell therapy. Among the deceased cohort, 58.3% (49/84) were hospitalized and 32.5% (26/80) received systemic therapy in the last 30 days of life. Rates of palliative care and hospice referrals were 47.6% and 30.9%, respectively. In multivariable logistic regression, receipt of bridging therapy (odds ratio [OR], 3.15; P=.041), index CAR-T hospitalization length of stay &gt;14 days (OR, 4.76; P=.009), hospital admission within 3 months of CAR T-cell infusion (OR, 4.29; P=.013), and indolent lymphoma transformed to diffuse large B-cell lymphoma (OR, 9.83; P=.012) were associated with likelihood of hospitalization in the last 30 days of life. Conclusions: A substantial minority of patients receiving CAR T-cell therapy experienced hospital readmission or ICU utilization in the first 3 months after CAR T-cell therapy, and most deceased recipients of CAR T-cell therapy received intensive EoL care. These findings underscore the need for interventions to optimize healthcare delivery and EoL care for this population.</jats:p

Differentials in air pollutant exposure across communities and regions in the UK

This is a report from the Air Quality Expert Group to the Department for Environment, Food and Rural Affairs; Scottish Government; Welsh Government; and Department of Agriculture, Environment and Rural Affairs in Northern Ireland, on the current state of scientific andtechnical knowledge of the differentials that exist in air pollution emissions and atmospheric concentrations across the United Kingdom and related issues of relevance to air quality management. The information contained within this report represents a review of the understanding and evidence available at the time of writing