Reduced-form and complex ACTM modelling for air quality policy development: A model inter-comparison

Oxley, Tim; Vieno, Massimo; Woodward, Huw; ApSimon, Helen; Mehlig, Daniel; Beck, Rachel; Nemitz, Eiko; Reis, Stefan

doi:10.1016/j.envint.2022.107676

Cited by 7 publications

(12 citation statements)

References 42 publications

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“…Emissions of isoprene and other biogenic VOC from vegetation, NO x from lightning and soil, wind-derived dust and sea salt, and marine dimethyl sulphide (DMS), are all linked to the meteorological year and simulated as reported in Simpson et al (2012) and model update reports specified above. UK emissions for model simulations of atmospheric composition in 2030, 2040 and 2050 use the 'business as usual (BAU)', also referred to as 'baseline', scenarios developed for Defra (ApSimon et al 2019;Defra, 2022;Oxley et al 2023). These projections reflect assumed trends under existing interventions and policies relating to air quality, including adjustments for recent policy that had not been incorporated into the 2018 NAEI projections.…”

Section: Air Pollution Modelling and Emissionsmentioning

confidence: 99%

Impacts of emissions policies on future UK mortality burdens associated with air pollution

Macintyre

Mitsakou²,

Vieno

et al. 2023

Environment International

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Section: Air Pollution Modelling and Emissionsmentioning

confidence: 99%

Impacts of emissions policies on future UK mortality burdens associated with air pollution

Macintyre

Mitsakou²,

Vieno

et al. 2023

Environment International

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“…These include non-exhaust emissions from road transport and domestic combustion of wood, which are examined further in Section 4, below. Additional uncertainties have been reported elsewhere [3,12], notably emissions from cooking which are not included in the NAEI but have been found to have a significant impact on exposure to PM2.5 [13], and the non-linear response of SIA to changes in pre-cursor emissions [14].…”

Section: Baseline Calculationsmentioning

confidence: 93%

“…The resulting concentrations are compared with measurements at national AURN background monitoring sites in Figure S.2 for 2018. There is some scatter, but the overall agreement is good with only a very small negative bias [14]. Here it is important to remember the uncertainties, including the additional interannual variability between years, whereas as UKIAM is based on annual average meteorological data.…”

Section: Model Validationmentioning

confidence: 95%

“…A comprehensive evaluation of the UKIAM has been reported elsewhere [14] which includes a direct comparison against the complex Eulerian EMEP4UK model for selected core scenarios reported here. This is important because UKIAM is a reduced-form model which can evaluate multiple scenarios quickly whereas EMEP4UK is a complex ACTM with full chemistry which has been tested widely against measurement data [15][16][17][18][19][20][21][22][23] and shows good performance.…”

Section: Model Validationmentioning

confidence: 99%

“…Throughout this work many uncertainties have been identified, with investigation where possible including model intercomparisons [14] and sensitivity studies, and suggestions for further work to refine the scenario assessment undertaken [3]. Improved information on emissions, including missing sources in the NAEI such as cooking and better data on wood-burning, can help to refine estimated concentrations and inform the setting of interim targets.…”

Section: Uncertaintiesmentioning

confidence: 99%

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Integrated Assessment Modelling of Future Air Quality in the UK to 2050, and Synergies with Net Zero Strategies

ApSimon¹,

Oxley²,

Woodward³

et al. 2023

Preprint

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Integrated assessment modelling (IAM) has been successfully used in the development of international agreements to reduce transboundary pollution in Europe, based on the GAINS model of IIASA. At a national level in the UK a similar approach has been taken with the UK Integrated Assessment Model, UKIAM, superimposing pollution abatement measures and behavioural change on energy projections designed to meet targets set for reduction of greenhouse gas emissions, and allowing for natural and imported contributions from other countries and shipping. This paper describes how UKIAM has been used in development of proposed targets for reduction of fine particulate PM2.5 in the Environment Act, exploring scenarios encompassing different levels of ambition up to 2050 with associated health and other environmental benefits. There are two PM2.5 targets, an annual mean concentration target setting a maximum concentration to be reached by a future year, and a population exposure reduction target with benefits for health across the whole population. The work goes further, to also demonstrate links to social deprivation. There is a strong connection between climate measures aimed at reducing net GHG emissions to zero by 2050 and future air quality, which may be positive or negative, as illustrated by sectoral studies for road transport where electrification of the fleet needs to match the evolution of energy production, and for domestic heating where use of wood for heating is an air quality issue. UKIAM has been validated against air pollution measurements and other modelling, but there are many uncertainties including future energy projections. New work is beginning to link UKIAM directly with the TIMES model addressing future energy projections, to explore different uptake scenarios for hydrogen production and use with respect to air quality.

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Long‐term exposure to fine particulate matter constituents, genetic susceptibility, and incident heart failure among 411 807 adults

Cui,

Zheng,

Zhang

et al. 2024

European J of Heart Fail

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AimsLong‐term fine particulate matter (PM2.5) exposure has been linked to incident heart failure (HF), but the impacts of its constituents remain unknown. We aimed to investigate the associations of PM2.5 constituents with incident HF, and further evaluate the modification effects of genetic susceptibility.Methods and resultsPM2.5 and its constituents, including elemental carbon (EC), organic matter (OM), ammonium (NH4+), nitrate (NO3−), and sulfate (SO42−), were estimated using the European Monitoring and Evaluation Programme model applied to the UK (EMEP4UK) driven by Weather and Research Forecast model meteorology. A polygenic risk score (PRS) was calculated to represent genetic susceptibility to HF. We employed Cox models to evaluate the associations of PM2.5 constituents with incident HF. Quantile‐based g‐computation model was used to identify the main contributor of PM2.5 constituents. Among 411 807 individuals in the UK Biobank, 7554 participants developed HF during a median follow‐up of 12.05 years. The adjusted hazard ratios of HF for each interquartile range increase in PM2.5, EC, OM, NH4+, NO3−, and SO42− were 1.50 (1.46–1.54), 1.31 (1.27–1.34), 1.12 (1.09–1.15), 1.42 (1.41–1.44), 1.26 (1.23–1.29), and 1.25 (1.24–1.26), respectively. EC (43%) played the most important role, followed by NH4+ and SO42−. Moreover, synergistic additive interactions accounted for 9–16% of the HF events in individuals exposed to both PM2.5, NH4+, NO3−, and SO42− and PRS.ConclusionLong‐term exposure to PM2.5 constituents may elevate HF risk, and EC was the major contributor. Additive effects of PM2.5 constituents and PRS on HF risk were revealed.

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Reduced-form and complex ACTM modelling for air quality policy development: A model inter-comparison

Cited by 7 publications

References 42 publications

Impacts of emissions policies on future UK mortality burdens associated with air pollution

Impacts of emissions policies on future UK mortality burdens associated with air pollution

Integrated Assessment Modelling of Future Air Quality in the UK to 2050, and Synergies with Net Zero Strategies

Long‐term exposure to fine particulate matter constituents, genetic susceptibility, and incident heart failure among 411 807 adults

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