Temperature-Dependent Evaporative Anthropogenic VOC Emissions Significantly Exacerbate Regional Ozone Pollution.

The evaporative emissions of anthropogenic volatile organic compounds (AVOCs) are sensitive to ambient temperature. This sensitivity forms an air pollution-meteorology connection that has not been assessed on a regional scale. We parametrized the temperature dependence of evaporative AVOC fluxes in a regional air quality model and evaluated the impacts on surface ozone in the Beijing-Tianjin-Hebei (BTH) area of China during the summer of 2017. The temperature dependency of AVOC emissions drove an enhanced simulated ozone-temperature sensitivity of 1.0 to 1.8 µg m-3 K-1, comparable to the simulated ozone-temperature sensitivity driven by the temperature dependency of biogenic VOC emissions (1.7 to 2.4 µg m-3 K-1). Ozone enhancements driven by temperature-induced AVOC increases were localized to their point of emission and were relatively more important in urban areas than in rural regions. The inclusion of the temperature-dependent AVOC emissions in our model improved the simulated ozone-temperature sensitivities on days of ozone exceedance. Our results demonstrated the importance of temperature-dependent AVOC emissions on surface ozone pollution and its heretofore unrepresented role in air pollution-meteorology interactions.

Amazon deforestation causes strong regional warming.

Tropical deforestation impacts the climate through complex land-atmosphere interactions causing local and regional warming. However, whilst the impacts of deforestation on local temperature are well understood, the regional (nonlocal) response is poorly quantified. Here, we used remote-sensed observations of forest loss and dry season land-surface temperature during the period 2001 to 2020 to demonstrate that deforestation of the Amazon caused strong warming at distances up to 100 km away from the forest loss. We apply a machine learning approach to show nonlocal warming due to forest loss at 2-100 km length scales increases the warming due to deforestation by more than a factor 4, from 0.16 K to 0.71 K for each 10-percentage points of forest loss. We estimate that rapid future deforestation under a strong inequality scenario could cause dry season warming of 0.96 K across Mato Grosso state in southern Brazil over the period 2020 to 2050. Reducing deforestation could reduce future warming caused by forest loss to 0.4 K. Our results demonstrate the contribution of tropical deforestation to regional climate warming and the potential for reduced deforestation to deliver regional climate adaptation and resilience with important implications for sustainable management of the Amazon.

Global distribution and climatic controls of natural mountain treelines.

Mountain treelines are thought to be sensitive to climate change. However, how climate impacts mountain treelines is not yet fully understood as treelines may also be affected by other human activities. Here, we focus on "closed-loop" mountain treelines (CLMT) that completely encircle a mountain and are less likely to have been influenced by human land-use change. We detect a total length of ~916,425 km of CLMT across 243 mountain ranges globally and reveal a bimodal latitudinal distribution of treeline elevations with higher treeline elevations occurring at greater distances from the coast. Spatially, we find that temperature is the main climatic driver of treeline elevation in boreal and tropical regions, whereas precipitation drives CLMT position in temperate zones. Temporally, we show that 70% of CLMT have moved upward, with a mean shift rate of 1.2 m/year over the first decade of the 21st century. CLMT are shifting fastest in the tropics (mean of 3.1 m/year), but with greater variability. Our work provides a new mountain treeline database that isolates climate impacts from other anthropogenic pressures, and has important implications for biodiversity, natural resources, and ecosystem adaptation in a changing climate.

Sensitivity of Air Pollution Exposure and Disease Burden to Emission Changes in China Using Machine Learning Emulation.

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 (PM2.5) and ozone (O3) 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 99.9% of the variance in PM2.5 and O3 concentrations. We used these emulators to estimate how emission reductions can attain air quality targets. In 2015, we estimate that PM2.5 exposure was 47.4 µg m-3 and O3 exposure was 43.8 ppb, associated with 2,189,700 (95% uncertainty interval, 95UI: 1,948,000-2,427,300) premature deaths per year, primarily from PM2.5 exposure (98%). PM2.5 exposure and the associated disease burden were most sensitive to industry and residential emissions. We explore the sensitivity of exposure and health to different combinations of emission reductions. The National Air Quality Target (35 µg m-3) for PM2.5 concentrations can be attained nationally with emission reductions of 72% in industrial, 57% in residential, 36% in land transport, 35% in agricultural, and 33% in power generation emissions. We show that complete removal of emissions from these five sectors does not enable the attainment of the WHO Annual Guideline (5 µg m-3) due to remaining air pollution from other sources. Our work provides the first assessment of how air pollution exposure and disease burden in China varies as emissions change across these five sectors and highlights the value of emulators in air quality research.

Emission Sector Impacts on Air Quality and Public Health in China From 2010 to 2020.

Anthropogenic emissions and ambient fine particulate matter (PM2.5) concentrations have declined in recent years across China. However, PM2.5 exposure remains high, ozone (O3) 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. We show that PM2.5 exposure peaked in 2012 at 52.8 µg m-3, with contributions of 31% from industry and 22% from residential emissions. In 2020, PM2.5 exposure declined by 36% to 33.5 µg m-3, where the contributions from industry and residential sources reduced to 15% and 17%, respectively. The PM2.5 disease burden decreased by only 9% over 2012 where the contributions from industry and residential sources reduced to 15% and 17%, respectively 2020, partly due to an aging population with greater susceptibility to air pollution. Most of the reduction in PM2.5 exposure and associated public health benefits occurred due to reductions in industrial (58%) and residential (29%) emissions. Reducing national PM2.5 exposure below the World Health Organization Interim Target 2 (25 µg m-3) would require a further 80% reduction in residential and industrial emissions, highlighting the challenges that remain to improve air quality in China.

Contrasting impacts of forests on cloud cover based on satellite observations.

Forests play a pivotal role in regulating climate and sustaining the hydrological cycle. The biophysical impacts of forests on clouds, however, remain unclear. Here, we use satellite data to show that forests in different regions have opposite effects on summer cloud cover. We find enhanced clouds over most temperate and boreal forests but inhibited clouds over Amazon, Central Africa, and Southeast US. The spatial variation in the sign of cloud effects is driven by sensible heating, where cloud enhancement is more likely to occur over forests with larger sensible heat, and cloud inhibition over forests with smaller sensible heat. Ongoing forest cover loss has led to cloud increase over forest loss hotspots in the Amazon (+0.78%), Indonesia (+1.19%), and Southeast US (+ 0.09%), but cloud reduction in East Siberia (-0.20%) from 2002-2018. Our data-driven assessment improves mechanistic understanding of forest-cloud interactions, which remain uncertain in Earth system models.

Achieving Brazil's Deforestation Target Will Reduce Fire and Deliver Air Quality and Public Health Benefits.

Climate, deforestation, and forest fires are closely coupled in the Amazon, but models of fire that include these interactions are lacking. We trained machine learning models on temperature, rainfall, deforestation, land-use, and fire data to show that spatial and temporal patterns of fire in the Amazon are strongly modified by deforestation. We find that fire count across the Brazilian Amazon increases by 0.44 percentage points for each percentage point increase in deforestation rate. We used the model to predict that the increased deforestation rate in the Brazilian Amazon from 2013 to 2020 caused a 42% increase in fire counts in 2020. We predict that if Brazil had achieved the deforestation target under the National Policy on Climate Change, there would have been 32% fewer fire counts across the Brazilian Amazon in 2020. Using a regional chemistry-climate model and exposure-response associations, we estimate that the improved air quality due to reduced smoke emission under this scenario would have resulted in 2,300 fewer deaths due to reduced exposure to fine particulate matter. Our analysis demonstrates the air quality and public health benefits that would accrue from reducing deforestation in the Brazilian Amazon.

Impact of the 2019/2020 Australian Megafires on Air Quality and Health.

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.

Air Pollution From Forest and Vegetation Fires in Southeast Asia Disproportionately Impacts the Poor.

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.

Large Air Quality and Public Health Impacts due to Amazonian Deforestation Fires in 2019.

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.

Statistical Emulation of Winter Ambient Fine Particulate Matter Concentrations From Emission Changes in China.

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.

Regional Policies Targeting Residential Solid Fuel and Agricultural Emissions Can Improve Air Quality and Public Health in the Greater Bay Area and Across China.

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.

Non-deforestation drivers of fires are increasingly important sources of aerosol and carbon dioxide emissions across Amazonia.

Deforestation rates have declined substantially across the Brazilian Legal Amazon (BLA) over the period from 2000-2017. However, reductions in fire, aerosol and carbon dioxide have been far less significant than deforestation, even when accounting for inter-annual variability in precipitation. Our observations and analysis support a decoupling between fire and deforestation that has exacerbated forest degradation in the BLA. Basing aerosol and carbon dioxide emissions on deforestation rates, without accounting for forest degradation will bias these important climate and ecosystem-health parameters low, both now and in the future. Recent increases in deforestation rate since 2014 will enhance such degradation, particularly during drought-conditions, increasing emissions of aerosol and greenhouse gases. Given Brazil's committed Nationally Determined Contribution under the Paris Agreement, failure to account for forest degradation fires will paint a false picture of prior progress and potentially have profound implications for both regional and global climate.

Have Synergies Between Nitrogen Deposition and Atmospheric CO2 Driven the Recent Enhancement of the Terrestrial Carbon Sink?

The terrestrial carbon sink has increased since the turn of this century at a time of increased fossil fuel burning, yet the mechanisms enhancing this sink are not fully understood. Here we assess the hypothesis that regional increases in nitrogen deposition since the early 2000s has alleviated nitrogen limitation and worked in tandem with enhanced CO2 fertilization to increase ecosystem productivity and carbon sequestration, providing a causal link between the parallel increases in emissions and the global land carbon sink. We use the Community Land Model (CLM4.5-BGC) to estimate the influence of changes in atmospheric CO2, nitrogen deposition, climate, and their interactions to changes in net primary production and net biome production. We focus on two periods, 1901-2016 and 1990-2016, to estimate changes in land carbon fluxes relative to historical and contemporary baselines, respectively. We find that over the historical period, nitrogen deposition (14%) and carbon-nitrogen synergy (14%) were significant contributors to the current terrestrial carbon sink, suggesting that long-term increases in nitrogen deposition led to a substantial increase in CO2 fertilization. However, relative to the contemporary baseline, changes in nitrogen deposition and carbon-nitrogen synergy had no substantial contribution to the 21st century increase in global carbon uptake. Nonetheless, we find that increased nitrogen deposition in East Asia since the early 1990s contributed 50% to the overall increase in net biome production over this region, highlighting the importance of carbon-nitrogen interactions. Therefore, potential large-scale changes in nitrogen deposition could have a significant impact on terrestrial carbon cycling and future climate.

Mixing State of Carbonaceous Aerosols of Primary Emissions from "Improved" African Cookstoves.

Designs of "improved" stoves are introduced recently to benefit the solid fuel consumption of cooking activities in developing countries, but the uncertainties concerning the combustion processes and particulate emissions remain poorly characterized. To help understand this, combustion in three examples of "improved" African cookstoves was investigated in the laboratory. A typical European heating stove was included for comparison purpose. Detailed aerosol emissions were studied in real-time with an Aerosol Mass Spectrometer and Single Particle Soot Photometer, to explore interactions between black carbon (BC) and organic carbon aerosols, which were parametrized according to modified combustion efficiency (MCE), a common metric used within the atmospheric emission community. Greater than 50% of the total organic matter (OM) was found in BC-containing particles when MCE was >0.95 for dry oak and coal fuels, whereas at lower MCE, over 80% of the total OM for most of the fuels existed in particles without detectable BC. When the OM mass fraction of total particulate matter (PM1) > 0.9, the mass ratio of OM to refractory BC in BC-containing particles was about 2-3, but only ∼0.8 when OM mass fraction <0.9. These findings are not currently included in models and such information should be considered in the future emission scenarios.

Residential energy use emissions dominate health impacts from exposure to ambient particulate matter in India.

Exposure to ambient fine particulate matter (PM2.5) is a leading contributor to diseases in India. Previous studies analysing emission source attributions were restricted by coarse model resolution and limited PM2.5 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 PM2.5 exposure in India. Observed annual mean PM2.5 concentrations exceed 100 µg m-3 and are well simulated by the model. We calculate that the emissions from residential energy use dominate (52%) population-weighted annual mean PM2.5 concentrations, and are attributed to 511,000 (95UI: 340,000-697,000) premature mortalities annually. However, removing residential energy use emissions would avert only 256,000 (95UI: 162,000-340,000), due to the non-linear exposure-response relationship causing health effects to saturate at high PM2.5 concentrations. Consequently, large reductions in emissions will be required to reduce the health burden from ambient PM2.5 exposure in India.

Current and Future Disease Burden From Ambient Ozone Exposure in India.

Long-term ambient ozone (O3) exposure is a risk factor for human health. We estimate the source-specific disease burden associated with long-term O3 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. We find that land transport emissions dominate the source contribution to this disease burden (35%), followed by emissions from power generation (23%). With no change in emissions by 2050, we estimate 1,126,000 (95UI: 421,000-1,667,000) annual premature mortalities, an increase of 200% relative to 2015 due to population aging and growth increasing the number of people susceptible to air pollution. We find that the International Energy Agency New Policy Scenario provides small changes (+1%) to this increasing disease burden from the demographic transition. Under the International Energy Agency Clean Air Scenario we estimate 791,000 (95UI: 202,000-1,336,000) annual premature mortalities in 2050, avoiding 335,000 annual premature mortalities (45% of the increase) compared to the scenario of no emission change. Our study highlights that critical public health benefits are possible with stringent emission reductions, despite population growth and aging increasing the attributable disease burden from O3 exposure even under such strong emission reductions. The disease burden attributable to ambient fine particulate matter exposure dominates that from ambient O3 exposure in the present day, while in the future, they may be similar in magnitude.

Stringent Emission Control Policies Can Provide Large Improvements in Air Quality and Public Health in India.

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.