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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). We found that eliminating fire could substantially improve regional air quality across Southeast Asia by reducing the population exposure to fine particulate matter (PM2.5) concentrations by 7% and surface ozone concentrations by 5%. These reductions in PM2.5 exposures would yield a considerable public health benefit across the region; averting 59,000 (95% uncertainty interval (95UI): 55,200–62,900) premature deaths annually. Analysis of subnational infant mortality rate data and PM2.5 exposure suggested that PM2.5 from fires disproportionately impacts poorer populations across Southeast Asia. We identified two key regions in northern Laos and western Myanmar where particularly high levels of poverty coincide with exposure to relatively high levels of PM2.5 from fires. Our results show that reducing forest and vegetation fires should be a public health priority for the Southeast Asia region., Key Points Eliminating forest and vegetation fires could substantially improve regional air quality in Mainland Southeast AsiaReducing exposure to particulate and ozone pollution from fires would yield a considerable public health benefit across Southeast AsiaParticulate air pollution from fires disproportionately impacts poorer populations across Southeast Asia

Air pollution from Amazon fires has adverse impacts on human health. The number of fires in the Amazon has increased in recent years, but whether this increase was driven by deforestation or climate has not been assessed. We analyzed relationships between fire, deforestation, and climate for the period 2003 to 2019 among selected states across the Brazilian Legal Amazon (BLA). A statistical model including deforestation, precipitation and temperature explained ∼80% of the variability in dry season fire count across states when totaled across the BLA, with positive relationships between fire count and deforestation. We estimate that the increase in deforestation since 2012 increased the dry season fire count in 2019 by 39%. Using a regional chemistry‐climate model combined with exposure‐response associations, we estimate this increase in fire resulted in 3,400 (95UI: 3,300–3,550) additional deaths in 2019 due to increased exposure to particulate air pollution. If deforestation in 2019 had increased to the maximum recorded during 2003–2019, the number of active fire counts would have increased by an additional factor of 2 resulting in 7,900 (95UI: 7,600–8,200) additional premature deaths. Our analysis demonstrates the strong benefits of reduced deforestation on air quality and public health across the Amazon., Key Points Increases in deforestation among states analyzed in the Brazilian Legal Amazon since 2012 increased the dry season fire count by 39% in 2019This increase in fire resulted in 3,400 (3,300–3,550) additional deaths in 2019 due to increased exposure to particulate air pollutionOur analysis demonstrates the strong benefits of reduced deforestation on air quality and public health across the Amazon

To improve poor air quality in Asia and inform effective emission-reduction strategies, it is vital to understand the contributions of different pollution sources and their associated human health burdens. In this study, we use the WRF-Chem regional atmospheric model to explore the air quality and human health benefits of eliminating emissions from six different anthropogenic sectors (transport, industry, shipping, electricity generation, residential combustion, and open biomass burning) over South and East Asia in 2014. We evaluate WRF-Chem against measurements from air quality monitoring stations across the region and find the model captures the spatial distribution and magnitude of PM2.5 (particulate matter with an aerodynamic diameter of no greater than 2.5 µm). We find that eliminating emissions from residential energy use, industry, or open biomass burning yields the largest reductions in population-weighted PM2.5 concentrations across the region. The largest human health benefit is achieved by eliminating either residential or industrial emissions, averting 467 000 (95 % uncertainty interval (95UI): 409 000–542 000) or 283 000 (95UI: 226 000–358 000) annual premature mortalities, respectively, in India, China, and South-east Asia, with fire prevention averting 28 000 (95UI: 24 000–32 000) annual premature mortalities across the region. We compare our results to previous sector-specific emission studies. Across these studies, residential emissions are the dominant cause of particulate pollution in India, with a multi-model mean contribution of 42 % to population-weighted annual mean PM2.5. Residential and industrial emissions cause the dominant contributions in China, with multi-model mean contributions of 29 % for both sectors to population-weighted annual mean PM2.5. Future work should focus on identifying the most effective options within the residential, industrial, and open biomass-burning emission sectors to improve air quality across South and East Asia.

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 (PM2.5) concentrations and the associated health impact from short‐term population exposure to bushfire PM2.5. The mean population Air Quality Index (AQI) exposure between September and February in the fires and no fires simulations indicates an additional ∼437,000 people were exposed to “Poor” or worse AQI levels due to the fires. The AQ impact was concentrated in the cities of Sydney, Newcastle‐Maitland, Canberra‐Queanbeyan and Melbourne. Between October and February 171 (95% CI: 66–291) deaths were brought forward due to short‐term exposure to bushfire PM2.5. The health burden was largest in New South Wales (NSW) (109 (95% CI: 41–176) deaths brought forward), Queensland (15 (95% CI: 5–24)), and Victoria (35 (95% CI: 13–56)). This represents 38%, 13% and 30% of the total deaths brought forward by short‐term exposure to all PM2.5. At a city‐level 65 (95% CI: 24–105), 23 (95% CI: 9–38) and 9 (95% CI: 4–14) deaths were brought forward from short‐term exposure to bushfire PM2.5, accounting for 36%, 20%, and 64% of the total deaths brought forward from all PM2.5. Thus, the bushfires caused substantial AQ and health impacts across eastern‐Australia. Climate change is projected to increase bushfire risk, therefore future fire management policies should consider this., Key Points The fires led to widespread exposure to “Poor” or worse Air Quality Index levels across eastern‐AustraliaThe highest all‐cause, all‐age mortality from short‐term exposure to bushfire particulate matter with a diameter less than 2.5 μm (PM2.5) was seen in the states of New South Wales, Queensland, and VictoriaAll‐cause, all‐age mortality from short‐term exposure to bushfire PM2.5 was highest in the cities of Sydney, Melbourne, and Canberra

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. We developed novel emulators relating emission changes in five key anthropogenic sectors (residential, industry, land transport, agriculture, and power generation) to winter ambient fine particulate matter (PM2.5) concentrations across China. The emulators were optimized based on Gaussian process regressors with Matern kernels. The emulators predicted 99.9% of the variance in PM2.5 concentrations for a given input configuration of emission changes. PM2.5 concentrations are primarily sensitive to residential (51%–94% of first‐order sensitivity index), industrial (7%–31%), and agricultural emissions (0%–24%). Sensitivities of PM2.5 concentrations to land transport and power generation emissions are all under 5%, except in South West China where land transport emissions contributed 13%. The largest reduction in winter PM2.5 exposure for changes in the five emission sectors is by 68%–81%, down to 15.3–25.9 μg m−3, remaining above the World Health Organization annual guideline of 10 μg m−3. The greatest reductions in PM2.5 exposure are driven by reducing residential and industrial emissions, emphasizing the importance of emission reductions in these key sectors. We show that the annual National Air Quality Target of 35 μg m−3 is unlikely to be achieved during winter without strong emission reductions from the residential and industrial sectors., Key Points We developed accurate and fast emulators to predict air quality in China from emission changesWinter ambient fine particulate matter concentrations were primarily sensitive to residential, industrial, and agricultural emissionsThe National Air Quality Target is unlikely to be met in winter without large emission reductions in the residential and industrial sectors

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 (PM2.5) exposure, with smaller contributions from ozone (O3) exposure. Recent emission reductions have reduced PM2.5 exposure. However, levels of exposure and the associated risk remain high, some pollutant emissions have increased, and some sectors lack effective emission control measures. We quantified the potential impacts of relevant policy scenarios on ambient air quality and public health across China. We show that PM2.5 exposure inside the Greater Bay Area (GBA) is strongly controlled by emissions outside the GBA. We find that reductions in residential solid fuel use and agricultural fertilizer emissions result in the greatest reductions in PM2.5 exposure and the largest health benefits. A 50% transition from residential solid fuel use to liquefied petroleum gas outside the GBA reduced PM2.5 exposure by 15% in China and 3% within the GBA, and avoided 191,400 premature deaths each year across China. Reducing agricultural fertilizer emissions of ammonia by 30% outside the GBA reduced PM2.5 exposure by 4% in China and 3% in the GBA, avoiding 56,500 annual premature deaths across China. Our simulations suggest that reducing residential solid fuel or industrial emissions will reduce both PM2.5 and O3 exposure, whereas other policies may increase O3 exposure. Improving particulate air quality inside the GBA will require consideration of residential solid fuel and agricultural sectors, which currently lack targeted policies, and regional cooperation both inside and outside the GBA., Key Points Ambient fine particulate matter exposure inside the Greater Bay Area is strongly controlled by emissions outside the Greater Bay AreaResidential solid fuel and agricultural emissions lack effective controls that could improve air quality and public health across ChinaImproving particulate air quality inside the Greater Bay Area will require regional cooperation inside and outside the Greater Bay Area

Vegetation fires across the tropics emit fine particulate matter (PM2.5) to the atmosphere, degrading regional air quality and impacting human health. Extensive vegetation fires occur regularly across the Amazon basin, but there have been no detailed assessments of the impacts on air quality or human health. We used updated exposure-response relationships and a regional climate-chemistry model, evaluated against a comprehensive set of observational data, to provide the first in-depth assessment of the potential public health benefits due to fire prevention across the Amazon Basin. We focused on 2012, a year with emissions similar to the 11-year average (2008 to 2018). Vegetation fires contributed >80% of simulated dry season mean surface PM2.5 in the western Amazon region particularly in Bolivia and Brazilian states of Rondônia, Acre, and Mato Grosso. We estimate that the prevention of vegetation fires would have averted 16 800 (95UI: 16 300–17 400) premature deaths and 641 000 (95UI: 551 900–741 300) disability adjusted life years (DALYs) across South America, with 26% of the avoided health burden located within the Amazon Basin. The health benefits of fire prevention in the Amazon are comparable to those found in Equatorial Asia.

On June 24th 2018 one of the largest UK wildfires in recent history broke out on Saddleworth Moor, close to Manchester, in north-west England. Since wildfires close to large populations in the UK have been relatively small and rare in the past, there is little knowledge about the impacts. This has prevented the development of effective strategies to reduce them. This paper uses a high-resolution coupled atmospheric-chemistry model to assess the impact of the fires on particulate matter with a diameter less than 2.5 µm (PM2.5) across the region and the impact on health from short-term exposure. We find that the fires substantially degraded air quality. PM2.5 concentrations increased by more than 300% in Oldham and Manchester and up to 50% in areas up to 80 km away such as Liverpool and Wigan. This led to one quarter of the population (2.9 million people) in the simulation domain (−4.9–0.7°E and 53.0–54.4°N) being exposed to moderate PM2.5 concentrations on at least one day, according to the Daily Air Quality Index (36–53 µg m−3), between June 23rd and 30th 2018. This equates to 4.5 million people being exposed to PM2.5 above the WHO 24-hour guideline of 25 µg m−3 on at least one day. Using a concentration-response function we calculate the short-term health impact, which indicates that in total over the 7-day period 28 (95% CI: 14.1–42.1) deaths were brought forward, with a mean daily excess mortality of 3.5 deaths per day (95% CI: 1.8–5.3). The excess mortalities from PM2.5 from the fires represented up to 60% of the total excess mortality (5.7 of 9.5 excess deaths), representing an increase of 3.8 excess mortalities (165% increase) compared to if there were no fires. We find the impact of mortality due to PM2.5 from the fires on the economy was also substantial (£21.1 m).

Air pollution is a serious environmental issue and leading contributor to disease burden in China. Rapid reductions in fine particulate matter (PM2.5) concentrations and increased ozone concentrations occurred across China during 2015 to 2017. We used measurements of particulate matter with a diameter

Exposure to high concentrations of ambient fine particulate matter (PM2.5) 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 PM2.5 concentrations and human health in India. We find that with no change in emissions, the disease burden from exposure to ambient PM2.5 in 2050 will increase by 75% relative to 2015, due to population aging and growth increasing the number of people susceptible to air pollution. We estimate that the International Energy Agencies New Policy Scenario (NPS) and Clean Air Scenario (CAS) in 2050 can reduce ambient PM2.5 concentrations below 2015 levels by 9% and 68%, respectively, offsetting 61,000 and 610,000 premature mortalities a year, which is 9% and 91% of the projected increase in premature mortalities due to population growth and aging. Throughout India, the CAS stands out as the most effective scenario to reduce ambient PM2.5 concentrations and the associated disease burden, reducing the 2050 mortality rate per 100,000 below 2015 control levels by 15%. However, even under such stringent emission control policies, population growth and aging results in premature mortality estimates from exposure to particulate air pollution to increase by 7% compared to 2015, highlighting the challenge facing efforts to improve public health in India., Key Points Air pollution is a major risk factor for human health in IndiaPopulation aging and growth will increase the disease burden due to exposure to particulate air pollution even under no emission changeStringent emission control reduces mortality rate in 2050 below 2015 levels although total premature mortality increases

Public health in India is gravely threatened by severe PM2.5 exposure. This study presents an analysis of long-term PM2.5 exposure in four Indian megacities (Delhi, Chennai, Hyderabad and Mumbai) based on in-situ observations during 2015–2018, and quantifies the health risks of short-term exposure during Diwali Fest (usually lasting for ~5 days in October or November and celebrating with lots of fireworks) in Delhi for the first time. The population-weighted annual-mean PM2.5 across the four cities was 72 μg/m3, ~3.5 times the global level of 20 μg/m3 and 1.8 times the annual criterion defined in the Indian National Ambient Air Quality Standards (NAAQS). Delhi suffers the worst air quality among the four cities, with citizens exposed to ‘severely polluted’ air for 10% of the time and to unhealthy conditions for 70% of the time. Across the four cities, long-term PM2.5 exposure caused about 28,000 (95% confidence interval: 17,200–39,400) premature mortality and 670,000 (428,900–935,200) years of life lost each year. During Diwali Fest in Delhi, average PM2.5 increased by ~75% and hourly concentrations reached 1676 μg/m3. These high pollutant levels led to an additional 20 (13–25) daily premature mortality in Delhi, an increase of 56% compared to the average over October–November. Distinct seasonal and diurnal variations in PM2.5 were found in all cities. PM2.5 mass concentrations peak during the morning rush hour in all cities. This indicates local traffic could be an important source of PM2.5, the control of which would be essential to improve air quality. We report an interesting seasonal variation in the diurnal pattern of PM2.5 concentrations, which suggests a 1–2 h shift in the morning rush hour from 8 a.m. in pre-monsoon/summer to 9–10 a.m. in winter. The difference between PM2.5 concentrations on weekdays and weekend, namely weekend effect, is negligible in Delhi and Hyderabad, but noticeable in Mumbai and Chennai where ~10% higher PM2.5 concentrations were observed in morning rush hour on weekdays. These local characteristics provide essential information for air quality modelling studies and are critical for tailoring the design of effective mitigation strategies for each city. Keywords: PM2.5, Health effect, Diwali festival effect, Weekend effect, Long-term, Short-term

Particulate matter (PM) emissions from vegetation and peat fires in Equatorial Asia cause poor regional air quality. Burning is greatest during drought years, resulting in strong inter-annual variability in emissions. We make the first consistent estimate of the emissions, air quality and public health impacts of Equatorial Asian fires during 2004–2015. The largest dry season (August—October) emissions occurred in 2015, with PM emissions estimated as 9.4 Tg, more than triple the average dry season emission (2.7 Tg). Fires in Sumatra and Kalimantan caused 94% of PM emissions from fires in Equatorial Asia. Peat combustion in Indonesian peatlands contributed 45% of PM emissions, with a greater contribution of 68% in 2015. We used the WRF-chem model to simulate dry season PM for the 6 biggest fire years during this period (2004, 2006, 2009, 2012, 2014, 2015). The model reproduces PM concentrations from a measurement network across Malaysia and Indonesia, suggesting our PM emissions are realistic. We estimate long-term exposure to PM resulted in 44 040 excess deaths in 2015, with more than 15 000 excess deaths annually in 2004, 2006, and 2009. Exposure to PM from dry season fires resulted in an estimated 131 700 excess deaths during 2004–2015. Our work highlights that Indonesian vegetation and peat fires frequently cause adverse impacts to public health across the region.

Each year more than 29,000 premature deaths in the UK are linked to long term-exposure to ambient particulate matter (PM) with a diameter less than 2.5 μm (PM2.5). Many studies have focused on the long-term impacts of exposure to PM, but short-term increases in pollution can also exacerbate health effects, leading to deaths brought forward within exposed populations. This study investigates the impact of different atmospheric circulation patterns on UK PM2.5 concentrations and the relative contribution of local and transboundary pollutants to variations in PM2.5 concentrations. Daily mean PM2.5 observations from 42 UK background sites indicate that easterly, south-easterly and southerly wind directions and anticyclonic circulation patterns enhance background concentrations of PM2.5 at all UK sites by up to 12 μg m-3. Results from back trajectory analysis and the European Monitoring and Evaluation Programme for UK model (EMEP4UK) show this is due to the transboundary transport of pollutants from continental Europe. While back trajectories indicate under easterly, south-easterly and southerly flow 25–50% of the total accumulated primary PM2.5 emissions originate outside of the UK, with a very polluted footprint (0.25–0.35 μg m-2). Anticyclonic conditions, which occur frequently (21%), also lead to increases in PM2.5 concentrations (UK multi-annual mean 14.7 μg m-3). EMEP4UK results indicate this is likely due the build-up of local emissions due to slack winds. Under westerly and north-westerly flow 15–30% of the total accumulated primary PM2.5 emissions originate outside of the UK, and are much less polluted (0.1 μg m-2) with model results indicating transport of clean maritime air masses from the Atlantic. Results indicate that both wind-direction and stability under anticyclonic conditions are important in controlling ambient PM2.5 concentrations across the UK. There is also a strong dependence of high PM2.5 Daily Air Quality Index (DAQI) values on easterly, south-easterly and southerly wind-directions, with >70% of occurrences of observed 48–71+ μg m-3 concentrations occurring under these wind directions. While north-westerly and cyclonic conditions reduce PM2.5 concentrations at all sites by up to 8 μg m-3. PM2.5 DAQI values are also lowest under these conditions, with >80% of 0–11 μg m-3 concentrations and >50% of 12–23 μg m-3 concentrations observed during westerly, north-westerly and northerly wind directions. Indicating that these conditions are likely to be associated with a reduction in the potential health effects from exposure to ambient levels of PM2.5. Keywords: LWT, PM2.5, long-range transport, Air quality, Emissions, AURN

Combustion of wood and other biomass is a significant contributor to poor air quality in many developing countries. Emissions of particulates and Polycyclic Aromatic Hydrocarbons (PAH) are a major health hazard, particularly in Africa where the use of domestic cookstoves has increased alongside population expansion. Because of economic factors firewood is commonly used in place of the more expensive charcoal; particularly in rural areas. This work conducts a study of PAH emissions from an African cookstove comparing the combustion of both charcoal and firewood. It is demonstrated that PAH and particulate emissions are much higher from the firewood compared to the charcoal. The difference in levels can be interpreted due to the importance of the pyrolysis reactions of the volatile components of wood in PAH formation, whereas these volatiles emissions are much smaller from charcoal. Analysis of the combustion phases (flaming, smouldering) is undertaken and a computer model has been developed to link the composition of the fuels to the emissions of the PAH and particulates. The modelled PAH levels are shown to follow a similar trend to the experimental results.