Publications by authors named "Stephen R Arnold"

Background: The World Health Organization identified climate change as the 21st century's biggest health threat. This study aimed to identify the current knowledge base, evidence gaps, and implications for climate action and health policymaking to address the health impact of climate change, including in the most underserved groups.

Methods: The Horizon-funded project ENBEL ('Enhancing Belmont Research Action to support EU policy making on climate change and health') organised a workshop at the 2021-European Public Health conference.

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Anthropogenic emissions and ambient fine particulate matter (PM) concentrations have declined in recent years across China. However, PM exposure remains high, ozone (O) exposure is increasing, and the public health impacts are substantial. We used emulators to explore how emission changes (averaged per sector over all species) have contributed to changes in air quality and public health in China over 2010-2020.

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Machine learning models can emulate chemical transport models, reducing computational costs and enabling more experimentation. We developed emulators to predict annual-mean fine particulate matter (PM) and ozone (O) concentrations and their associated chronic health impacts from changes in five major emission sectors (residential, industrial, land transport, agriculture, and power generation) in China. The emulators predicted 99.

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The Australian 2019/2020 bushfires were unprecedented in their extent and intensity, causing a catastrophic loss of habitat, human and animal life across eastern-Australia. We use a regional air quality model to assess the impact of the bushfires on particulate matter with a diameter less than 2.5 μm (PM) concentrations and the associated health impact from short-term population exposure to bushfire PM.

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Forest and vegetation fires, used as tools for agriculture and deforestation, are a major source of air pollutants and can cause serious air quality issues in many parts of Asia. Actions to reduce fire may offer considerable, yet largely unrecognized, options for rapid improvements in air quality. In this study, we used a combination of regional and global air quality models and observations to examine the impact of forest and vegetation fires on air quality degradation and public health in Southeast Asia (including Mainland Southeast Asia and south-eastern China).

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Air pollution exposure remains a leading public health problem in China. The use of chemical transport models to quantify the impacts of various emission changes on air quality is limited by their large computational demands. Machine learning models can emulate chemical transport models to provide computationally efficient predictions of outputs based on statistical associations with inputs.

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Air pollution exposure is a leading public health problem in China. The majority of the total air pollution disease burden is from fine particulate matter (PM) exposure, with smaller contributions from ozone (O) exposure. Recent emission reductions have reduced PM exposure.

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The global oxidation capacity, defined as the tropospheric mean concentration of the hydroxyl radical (OH), controls the lifetime of reactive trace gases in the atmosphere such as methane and carbon monoxide (CO). Models tend to underestimate the methane lifetime and CO concentrations throughout the troposphere, which is consistent with excessive OH. Approximately half of the oxidation of methane and non-methane volatile organic compounds (VOCs) is thought to occur over the oceans where oxidant chemistry has received little validation due to a lack of observational constraints.

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Long-term ambient ozone (O) exposure is a risk factor for human health. We estimate the source-specific disease burden associated with long-term O exposure in India at high spatial resolution using updated risk functions from the American Cancer Society Cancer Prevention Study II. We estimate 374,000 (95UI: 140,000-554,000) annual premature mortalities using the updated risk function in India in 2015, 200% larger than estimates using the earlier American Cancer Society Cancer Prevention Study II risk function.

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Exposure to high concentrations of ambient fine particulate matter (PM) is a leading risk factor for public health in India causing a large burden of disease. Business-as-usual economic and industrial growth in India is predicted to increase emissions, worsen air quality, and increase the associated disease burden in future decades. Here we use a high-resolution online-coupled model to estimate the impacts of different air pollution control pathways on ambient PM concentrations and human health in India.

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Exposure to ambient fine particulate matter (PM) is a leading contributor to diseases in India. Previous studies analysing emission source attributions were restricted by coarse model resolution and limited PM observations. We use a regional model informed by new observations to make the first high-resolution study of the sector-specific disease burden from ambient PM exposure in India.

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