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Global anthropogenic emission inventories remain vital for understanding the sources of atmospheric pollution and the associated impacts on the environment, human health, and society. Rapid changes in today's society require that these inventories provide contemporary estimates of multiple atmospheric pollutants with both source sector and fuel type information to understand and effectively mitigate future impacts. To fill this need, we have updated the open-source Community Emissions Data System (CEDS) (Hoesly et al., 2019) to develop a new global emission inventory, CEDSGBD-MAPS. This inventory includes emissions of seven key atmospheric pollutants (NOx; CO; SO2; NH3; non-methane volatile organic compounds, NMVOCs; black carbon, BC; organic carbon, OC) over the time period from 1970–2017 and reports annual country-total emissions as a function of 11 anthropogenic sectors (agriculture; energy generation; industrial processes; on-road and non-road transportation; separate residential, commercial, and other sectors (RCO); waste; solvent use; and international shipping) and four fuel categories (total coal, solid biofuel, the sum of liquid-fuel and natural-gas combustion, and remaining process-level emissions). The CEDSGBD-MAPS inventory additionally includes monthly global gridded (0.5∘ × 0.5∘) emission fluxes for each compound, sector, and fuel type to facilitate their use in earth system models. CEDSGBD-MAPS utilizes updated activity data, updates to the core CEDS default scaling procedure, and modifications to the final procedures for emissions gridding and aggregation. Relative to the previous CEDS inventory (Hoesly et al., 2018), these updates extend the emission estimates from 2014 to 2017 and improve the overall agreement between CEDS and two widely used global bottom-up emission inventories. The CEDSGBD-MAPS inventory provides the most contemporary global emission estimates to date for these key atmospheric pollutants and is the first to provide global estimates for these species as a function of multiple fuel types and source sectors. Dominant sources of global NOx and SO2 emissions in 2017 include the combustion of oil, gas, and coal in the energy and industry sectors as well as on-road transportation and international shipping for NOx. Dominant sources of global CO emissions in 2017 include on-road transportation and residential biofuel combustion. Dominant global sources of carbonaceous aerosol in 2017 include residential biofuel combustion, on-road transportation (BC only), and emissions from the waste sector. Global emissions of NOx, SO2, CO, BC, and OC all peak in 2012 or earlier, with more recent emission reductions driven by large changes in emissions from China, North America, and Europe. In contrast, global emissions of NH3 and NMVOCs continuously increase between 1970 and 2017, with agriculture as a major source of global NH3 emissions and solvent use, energy, residential, and the on-road transport sectors as major sources of global NMVOCs. Due to similar development methods and underlying datasets, the CEDSGBD-MAPS emissions are expected to have consistent sources of uncertainty as other bottom-up inventories. The CEDSGBD-MAPS source code is publicly available online through GitHub: https://github.com/emcduffie/CEDS/tree/CEDS_GBD-MAPS (last access: 1 December 2020). The CEDSGBD-MAPS emission inventory dataset (both annual country-total and monthly global gridded files) is publicly available under https://doi.org/10.5281/zenodo.3754964 (McDuffie et al., 2020c).

Ambient fine particulate matter (PM 2.5 ) is the world’s leading environmental health risk factor. Reducing the PM 2.5 disease burden requires specific strategies that target dominant sources across multiple spatial scales. We provide a contemporary and comprehensive evaluation of sector- and fuel-specific contributions to this disease burden across 21 regions, 204 countries, and 200 sub-national areas by integrating 24 global atmospheric chemistry-transport model sensitivity simulations, high-resolution satellite-derived PM 2.5 exposure estimates, and disease-specific concentration response relationships. Globally, 1.05 (95% Confidence Interval: 0.74–1.36) million deaths were avoidable in 2017 by eliminating fossil-fuel combustion (27.3% of the total PM 2.5 burden), with coal contributing to over half. Other dominant global sources included residential (0.74 [0.52–0.95] million deaths; 19.2%), industrial (0.45 [0.32–0.58] million deaths; 11.7%), and energy (0.39 [0.28–0.51] million deaths; 10.2%) sectors. Our results show that regions with large anthropogenic contributions generally had the highest attributable deaths, suggesting substantial health benefits from replacing traditional energy sources. Ambient fine particulate matter (PM 2.5 ) is one of the most important environmental health risk factors in many regions. Here, the authors present an assessment of PM 2.5 emission sources and the related health impacts across global to sub-national scales and find that over 1 million deaths were avoidable in 2017 by eliminating PM 2.5 mass associated with fossil fuel combustion emissions.

Ambient fine particulate matter (PM 2.5 ) is the world’s leading environmental health risk factor. Quantification is needed of regional contributions to changes in global PM 2.5 exposure. Here we interpret satellite-derived PM 2.5 estimates over 1998-2019 and find a reversal of previous growth in global PM 2.5 air pollution, which is quantitatively attributed to contributions from 13 regions. Global population-weighted (PW) PM 2.5 exposure, related to both pollution levels and population size, increased from 1998 (28.3 μg/m 3 ) to a peak in 2011 (38.9 μg/m 3 ) and decreased steadily afterwards (34.7 μg/m 3 in 2019). Post-2011 change was related to exposure reduction in China and slowed exposure growth in other regions (especially South Asia, the Middle East and Africa). The post-2011 exposure reduction contributes to stagnation of growth in global PM 2.5 -attributable mortality and increasing health benefits per µg/m 3 marginal reduction in exposure, implying increasing urgency and benefits of PM 2.5 mitigation with aging population and cleaner air. Global fine particulate matter air pollution recently pivots from increase to decrease as inferred from satellite observations, driven by unprecedented exposure reduction in China and slowed exposure growth in South Asia, the Middle East and Africa.

Over the last decades, air pollution emissions have decreased substantially; however, inequities in air pollution persist. We evaluate county-level racial/ethnic and socioeconomic disparities in emissions changes from six air pollution source sectors (industry [SO 2 ], energy [SO 2 , NO x ], agriculture [NH 3 ], commercial [NO x ], residential [particulate organic carbon], and on-road transportation [NO x ]) in the contiguous United States during the 40 years following the Clean Air Act (CAA) enactment (1970-2010). We calculate relative emission changes and examine the differential changes given county demographics using hierarchical nested models. The results show racial/ethnic disparities, particularly in the industry and energy generation source sectors. We also find that median family income is a driver of variation in relative emissions changes in all sectors—counties with median family income >$75 K vs. less generally experience larger relative declines in industry, energy, transportation, residential, and commercial-related emissions. Emissions from most air pollution source sectors have, on a national level, decreased following the United States CAA. In this work, we show that the relative reductions in emissions varied across racial/ethnic and socioeconomic groups. Here the authors find socioeconomic and racial/ethnic disparities in county-level air pollution emissions reductions in the 40 years following the Clean Air Act (1970-2010) in the USA, particularly in emissions from energy generation and industry.

Emissions into the atmosphere of fine particulate matter, its precursors, and precursors to tropospheric ozone impact not only human health and ecosystems, but also the climate by altering Earth's radiative balance. Accurately quantifying these impacts across local to global scales historically and in future scenarios requires emission inventories that are accurate, transparent, complete, comparable, and consistent. In an effort to better quantify the emissions and impacts of these pollutants, also called short-lived climate forcers (SLCFs), the Intergovernmental Panel on Climate Change (IPCC) is developing a new SLCF emissions methodology report. This report would supplement existing IPCC reporting guidance on greenhouse gas (GHG) emission inventories, which are currently used by inventory compilers to fulfill national reporting requirements under the United Nations Framework Convention on Climate Change (UNFCCC) and new requirements of the Enhanced Transparency Framework (ETF) under the Paris Agreement starting in 2024. We review the relevant issues, including how air pollutant and GHG inventory activities have historically been structured, as well as potential benefits, challenges, and recommendations for coordinating GHG and air pollutant inventory efforts. We argue that, while there are potential benefits to increasing coordination between air pollutant and GHG inventory development efforts, we also caution that there are differences in appropriate methodologies and applications that must jointly be considered.

Emission inventory development for air pollutants, by compiling records from individual emission sources, takes many years and involves extensive multi-national effort. A complementary method to estimate air pollution emissions is in the use of satellite remote sensing. In this study, NO 2 observations from the Ozone Monitoring Instrument are combined with re-analysis meteorology to estimate urban nitrogen oxide (NO X ) emissions for 80 global cities between 2005 and 2019. The global average downward trend in satellite-derived urban NO X emissions was 3.1%–4.0% yr −1 between 2009 and 2018 while inventories show a 0%–2.2% yr −1 drop over the same timeframe. This difference is primarily driven by discrepancies between satellite-derived urban NO X emissions and inventories in Africa, China, India, Latin America, and the Middle East. In North America, Europe, Korea, Japan, and Australasia, NO X emissions dropped similarly as reported in the inventories. In Europe, Korea, and Japan only, the temporal trends match the inventories well, but the satellite estimate is consistently larger over time. While many of the discrepancies between satellite-based and inventory emissions estimates represent real differences, some of the discrepancies might be related to the assumptions made to compare the satellite-based estimates with inventory estimates, such as the spatial disaggregation of emissions inventories. Our work identifies that the three largest uncertainties in the satellite estimate are the tropospheric column measurements, wind speed and direction, and spatial definition of each city.

Developing future climate projections begins with choosing future emissions scenarios. While scenarios are often based on storylines, here instead we produce a probabilistic multi-million-member ensemble of radiative forcing trajectories to assess the relevance of future forcing thresholds. We coupled a probabilistic database of future greenhouse gas emission scenarios with a probabilistically calibrated reduced complexity climate model. In 2100, we project median forcings of 5.1 watt per square meters (5th to 95th percentiles of 3.3 to 7.1), with roughly 0.5% probability of exceeding 8.5 watt per square meters, and a 1% probability of being lower than 2.6 watt per square meters. Although the probability of 8.5 watt per square meters scenarios is low, our results support their continued utility for calibrating damage functions, characterizing climate in the 22 nd century (the probability of exceeding 8.5 watt per square meters increases to about 7% by 2150), and assessing low-probability/high-impact futures. The probability of exceeding various climate thresholds has been calculated. While the warmest future scenario has less than a 1 percent chance of being exceeded this century, the paper discusses why such warm scenarios are still relevant.

Airborne and ground-based measurements of aerosol concentrations, chemical composition, and gas-phase precursors were obtained in three valleys in northern Utah (USA). The measurements were part of the Utah Winter Fine Particulate Study (UWFPS) that took place in January–February 2017. Total aerosol mass concentrations of PM1 were measured from a Twin Otter aircraft, with an aerosol mass spectrometer (AMS). PM1 concentrations ranged from less than 2 µg m−3 during clean periods to over 100 µg m−3 during the most polluted episodes, consistent with PM2.5 total mass concentrations measured concurrently at ground sites. Across the entire region, increases in total aerosol mass above ∼2 µg m−3 were associated with increases in the ammonium nitrate mass fraction, clearly indicating that the highest aerosol mass loadings in the region were predominantly attributable to an increase in ammonium nitrate. The chemical composition was regionally homogenous for total aerosol mass concentrations above 17.5 µg m−3, with 74±5 % (average ± standard deviation) ammonium nitrate, 18±3 % organic material, 6±3 % ammonium sulfate, and 2±2 % ammonium chloride. Vertical profiles of aerosol mass and volume in the region showed variable concentrations with height in the polluted boundary layer. Higher average mass concentrations were observed within the first few hundred meters above ground level in all three valleys during pollution episodes. Gas-phase measurements of nitric acid (HNO3) and ammonia (NH3) during the pollution episodes revealed that in the Cache and Utah valleys, partitioning of inorganic semi-volatiles to the aerosol phase was usually limited by the amount of gas-phase nitric acid, with NH3 being in excess. The inorganic species were compared with the ISORROPIA thermodynamic model. Total inorganic aerosol mass concentrations were calculated for various decreases in total nitrate and total ammonium. For pollution episodes, our simulations of a 50 % decrease in total nitrate lead to a 46±3 % decrease in total PM1 mass. A simulated 50 % decrease in total ammonium leads to a 36±17 % µg m−3 decrease in total PM1 mass, over the entire area of the study. Despite some differences among locations, our results showed a higher sensitivity to decreasing nitric acid concentrations and the importance of ammonia at the lowest total nitrate conditions. In the Salt Lake Valley, both HNO3 and NH3 concentrations controlled aerosol formation.

Atmospheric methane directly affects surface temperatures and indirectly affects ozone, impacting human welfare, the economy, and environment. The social cost of methane (SC‐CH4) metric estimates the costs associated with an additional marginal metric ton of emissions. Current SC‐CH4 estimates do not consider the indirect impacts associated with ozone production from changes in methane. We use global model simulations and a new BenMAP webtool to estimate respiratory‐related deaths associated with increases in ozone from a pulse of methane emissions in 2020. By using an approach consistent with the current SC‐CH4 framework, we monetize and discount annual damages back to present day values. We estimate that the methane‐ozone mechanism is attributable to 760 (95% CI: 330–1200) respiratory‐related deaths per million metric tons of methane globally, for a global net present damage of$1800/mT (95% CI: $ 760– $2800/mT CH4; 2% Ramsey discount rate); this would double the current SC‐CH4 if included. These physical impacts are consistent with recent studies, but comparing direct costs is challenging. Economic damages are sensitive to uncertainties in the exposure and health risks associated with tropospheric ozone, assumptions about future projections of NOx emissions, socioeconomic conditions, and mortality rates, monetization parameters, and other factors. Our estimates are highly sensitive to uncertainties in ozone health risks. We also develop a reduced form model to test sensitivities to other parameters. The reduced form tool runs with a user‐supplied emissions pulse, as well as socioeconomic and precursor projections, enabling future integration of the methane‐ozone mechanism into the SC‐CH4 modeling framework. Plain Language Summary The social cost of methane is used to assess the costs and benefits associated with emissions mitigation in U.S. regulations, in addition to other decision‐making applications. The current social cost of methane used by the U.S. Government is $ 1500/metric ton of methane emissions. This estimate does not include damages related to deaths associated with changes in exposure to background ozone, resulting from increases in atmospheric methane. Using an approach consistent with the social cost of methane framework, we estimate that damages from the methane‐ozone mechanism are$1800/metric ton, which, if included, would double the current social cost of methane. These costs have uncertainties related to the health risks associated with exposure to ozone, assumptions about future NOx emissions, choice of discount rates, and other factors. We also develop a reduced form model that allows rapid estimation of many of these sensitivities and enables consideration of this mechanism in the social cost methodology. Key Points Increases in mortality attributable to ozone produced from methane are not currently considered in the government's social cost of methane Ozone from a 2020 methane emissions pulse results in 760 deaths per million metric ton and a net present value of $ 1800 per metric ton A reduced form tool is developed to assess uncertainties and facilitate additional social cost of methane calculations

High levels of fine particulate matter (PM2.5) pollution in East Asia often exceed local air quality standards. Observations from the Korea–United States Air Quality (KORUS-AQ) field campaign in May and June 2016 showed that development of extreme pollution (haze) occurred through a combination of long-range transport and favorable meteorological conditions that enhanced local production of PM2.5. Atmospheric models often have difficulty simulating PM2.5 chemical composition during haze, which is of concern for the development of successful control measures. We use observations from KORUS-AQ to examine the ability of the GEOS-Chem chemical transport model to simulate PM2.5 composition throughout the campaign and identify the mechanisms driving the pollution event. At the surface, the model underestimates sulfate by -64 % but overestimates nitrate by +36 %. The largest underestimate in sulfate occurs during the pollution event, for which models typically struggle to generate elevated sulfate concentrations due to missing heterogeneous chemistry in aerosol liquid water in the polluted boundary layer. Hourly surface observations show that the model nitrate bias is driven by an overestimation of the nighttime peak. In the model, nitrate formation is limited by the supply of nitric acid, which is biased by +100 % against aircraft observations. We hypothesize that this is due to a large missing sink, which we implement here as a factor of 5 increase in dry deposition. We show that the resulting increased deposition velocity is consistent with observations of total nitrate as a function of photochemical age. The model does not account for factors such as the urban heat island effect or the heterogeneity of the built-up urban landscape, resulting in insufficient model turbulence and surface area over the study area that likely results in insufficient dry deposition. Other species such as NH3 could be similarly affected but were not measured during the campaign. Nighttime production of nitrate is driven by NO2 hydrolysis in the model, while observations show that unexpectedly elevated nighttime ozone (not present in the model) should result in N2O5 hydrolysis as the primary pathway. The model is unable to represent nighttime ozone due to an overly rapid collapse of the afternoon mixed layer and excessive titration by NO. We attribute this to missing nighttime heating driving deeper nocturnal mixing that would be expected to occur in a city like Seoul. This urban heating is not considered in air quality models run at large enough scales to treat both local chemistry and long-range transport. Key model failures in simulating nitrate, mainly overestimated daytime nitric acid, incorrect representation of nighttime chemistry, and an overly shallow and insufficiently turbulent nighttime mixed layer, exacerbate the model's inability to simulate the buildup of PM2.5 during haze pollution. To address the underestimate in sulfate most evident during the haze event, heterogeneous aerosol uptake of SO2 is added to the model, which previously only considered aqueous production of sulfate from SO2 in cloud water. Implementing a simple parameterization of this chemistry improves the model abundance of sulfate but degrades the SO2 simulation, implying that emissions are underestimated. We find that improving model simulations of sulfate has direct relevance to determining local vs. transboundary contributions to PM2.5. During the haze pollution event, the inclusion of heterogeneous aerosol uptake of SO2 decreases the fraction of PM2.5 attributable to long-range transport from 66 % to 54 %. Locally produced sulfate increased from 1 % to 25 % of locally produced PM2.5, implying that local emissions controls could have a larger effect than previously thought. However, this additional uptake of SO2 is coupled to the model nitrate prediction, which affects the aerosol liquid water abundance and chemistry driving sulfate–nitrate–ammonium partitioning. An additional simulation of the haze pollution with heterogeneous uptake of SO2 to aerosol and simple improvements to the model nitrate simulation results in 30 % less sulfate due to 40 % less nitrate and aerosol water, and this results in an underestimate of sulfate during the haze event. Future studies need to better consider the impact of model physical processes such as dry deposition and nighttime boundary layer mixing on the simulation of nitrate and the effect of improved nitrate simulations on the overall simulation of secondary inorganic aerosol (sulfate + nitrate + ammonium) in East Asia. Foreign emissions are rapidly changing, increasing the need to understand the impact of local emissions on PM2.5 in South Korea to ensure continued air quality improvements.