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Atmospheric methane concentrations rose rapidly over the past decade and surged in 2020-2022 but the causes have been unclear. We find from inverse analysis of GOSAT satellite observations that emissions from the wet tropics drove the 2010-2019 increase and the subsequent 2020-2022 surge, while emissions from northern mid-latitudes decreased. The 2020-2022 surge is principally contributed by emissions in Equatorial Asia (43%) and Africa (30%). Wetlands are the major drivers of the 2020-2022 emission increases in Africa and Equatorial Asia because of tropical inundation associated with La Niña conditions, consistent with trends in the GRACE terrestrial water storage data. In contrast, emissions from major anthropogenic emitters such as the United States, Russia, and China are relatively flat over 2010-2022. Concentrations of tropospheric OH (the main methane sink) show no long-term trend over 2010-2022 but a decrease over 2020-2022 that contributed to the methane surge. [ABSTRACT FROM AUTHOR]

China's sustained air quality improvement is hindered by unregulated ammonia (NH3) emissions from inefficient nitrogen management in smallholder farming. Although the Chinese government is promoting a policy shift to large-scale farming, the benefits of this, when integrated with nitrogen management, remain unclear. Here we fill this gap using an integrated assessment, by combining geostatistical analysis, high-resolution emission inventories, farm surveys and air quality modeling. Smallholder-dominated farming allows only 13%–31% NH3 reduction, leading to limited PM2.5 decreases nationally due to non-linear PM2.5 chemistry. Conversely, large-scale farming would double nitrogen management adoption rates, increasing NH3 reduction potential to 48%–58% and decreasing PM2.5 by 9.4–14.0 μg·m−3 in polluted regions. The estimated PM2.5 reduction is conservative due to localized NH3-rich conditions under large-scale livestock farming. This strategy could prevent over 300 000 premature deaths and achieve a net benefit of US $68.4–86.8 billion annually, unlocking immense benefits for air quality and agricultural sustainability. [ABSTRACT FROM AUTHOR]

The Geostationary Environment Monitoring Spectrometer (GEMS) launched in February 2020 is now providing continuous daytime hourly observations of nitrogen dioxide (NO 2) columns over eastern Asia (5° S–45° N, 75–145° E) with 3.5 × 7.7 km 2 pixel resolution. These data provide unique information to improve understanding of the sources, chemistry, and transport of nitrogen oxides (NO x) with implications for atmospheric chemistry and air quality, but opportunities for direct validation are very limited. Here we correct the operational level-2 (L2) NO 2 vertical column densities (VCDs) from GEMS with a machine learning (ML) model to match the much sparser but more mature observations from the low Earth orbit TROPOspheric Monitoring Instrument (TROPOMI), preserving the data density of GEMS but making them consistent with TROPOMI. We first reprocess the GEMS and TROPOMI operational L2 products to use common prior vertical NO 2 profiles (shape factors) from the GEOS-Chem chemical transport model. This removes a major inconsistency between the two satellite products and greatly improves their agreement with ground-based Pandora NO 2 VCD data in source regions. We then apply the ML model to correct the remaining differences, Δ (GEMS–TROPOMI), using the GEMS NO 2 VCDs and retrieval parameters as predictor variables. We train the ML model with colocated GEMS and TROPOMI NO 2 VCDs, taking advantage of TROPOMI off-track viewing to cover the wide range of effective zenith angles (EZAs) observed by GEMS. The two most important predictor variables for Δ (GEMS–TROPOMI) are GEMS NO 2 VCD and EZA. The corrected GEMS product is unbiased relative to TROPOMI and shows a diurnal variation over source regions more consistent with Pandora than the operational product. [ABSTRACT FROM AUTHOR]

Tropospheric ozone is a major air pollutant and greenhouse gas. It is also the primary precursor of OH, the main tropospheric oxidant. Global atmospheric chemistry models show large differences in their simulations of tropospheric ozone budgets. Here we implement the widely used GEOS-Chem atmospheric chemistry module as an alternative to CAM-chem within the Community Earth System Model version 2 (CESM2). We compare the resulting GEOS-Chem and CAM-chem simulations of tropospheric ozone and related species within CESM2 to observations from ozonesondes, surface sites, the ATom-1 aircraft campaign over the Pacific and Atlantic, and the KORUS-AQ aircraft campaign over the Seoul Metropolitan Area. We find that GEOS-Chem and CAM-chem within CESM2 have similar tropospheric ozone budgets and concentrations usually within 5 ppb but important differences in the underlying processes including (1) photolysis scheme (no aerosol effects in CAM-chem), (2) aerosol nitrate photolysis, (3) N2O5 cloud uptake, (4) tropospheric halogen chemistry, and (5) ozone deposition to the oceans. Global tropospheric OH concentrations are the same in both models, but there are large regional differences reflecting the above processes. Carbon monoxide is lower in CAM-chem (and lower than observations), at least in part because of higher OH concentrations in the Northern Hemisphere and insufficient production from isoprene oxidation in the Southern Hemisphere. CESM2 does not scavenge water-soluble gases in convective updrafts, leading to some upper-tropospheric biases. Comparison to KORUS-AQ observations shows an overestimate of ozone above 4 km altitude in both models, which at least in GEOS-Chem is due to inadequate scavenging of particulate nitrate in convective updrafts in CESM2, leading to excessive NO production from nitrate photolysis. The KORUS-AQ comparison also suggests insufficient boundary layer mixing in CESM2. This implementation and evaluation of GEOS-Chem in CESM2 contribute to the MUSICA vision of modularizing tropospheric chemistry in Earth system models. [ABSTRACT FROM AUTHOR]

We show that the Landsat and Sentinel-2 satellites can detect NO2 plumes from large point sources at 10 to 60 m pixel resolution in their blue and ultrablue bands. We use the resulting NO2 plume imagery to quantify nitrogen oxides (NOx) emission rates for several power plants in Saudi Arabia and the United States, including a 13-y analysis of 132 Landsat plumes from Riyadh power plant 9 from 2009 through 2021. NO2 in the plumes initially increases with distance from the source, likely reflecting recovery from ozone titration. The fine pixel resolutions of Landsat and Sentinel-2 enable separation of individual point sources and stacks, including in urban background, and the long records enable examination of multidecadal emission trends. Our inferred NOx emission rates are consistent with previous estimates to within a precision of about 30%. Sources down to ~500 kg h-1 can be detected over bright, quasi-homogeneous surfaces. The 2009 to 2021 data for Riyadh power plant 9 show a strong summer peak in emissions, consistent with increased power demand for air conditioning, and a marginal slow decrease following the introduction of Saudi Arabia's Ambient Air Standard 2012. [ABSTRACT FROM AUTHOR]

Nitrogen oxide radicals (NOx≡NO+NO2) emitted by fuel combustion are important precursors of ozone and particulate matter pollution, and NO 2 itself is harmful to public health. The Geostationary Environment Monitoring Spectrometer (GEMS), launched in space in 2020, now provides hourly daytime observations of NO 2 columns over East Asia. This diurnal variation offers unique information on the emission and chemistry of NOx , but it needs to be carefully interpreted. Here we investigate the drivers of the diurnal variation in NO 2 observed by GEMS during winter and summer over Beijing and Seoul. We place the GEMS observations in the context of ground-based column observations (Pandora instruments) and GEOS-Chem chemical transport model simulations. We find good agreement between the diurnal variations in NO 2 columns in GEMS, Pandora, and GEOS-Chem, and we use GEOS-Chem to interpret these variations. NOx emissions are 4 times higher in the daytime than at night, driving an accumulation of NO 2 over the course of the day, offset by losses from chemistry and transport (horizontal flux divergence). For the urban core, where the Pandora instruments are located, we find that NO 2 in winter increases throughout the day due to high daytime emissions and increasing NO2/NOx ratio from entrainment of ozone, partly balanced by loss from transport and with a negligible role of chemistry. In summer, by contrast, chemical loss combined with transport drives a minimum in the NO 2 column at 13:00–14:00 local time (LT). Segregation of the GEMS data by wind speed further demonstrates the effect of transport, with NO 2 in winter accumulating throughout the day at low winds but flat at high winds. The effect of transport can be minimized in summer by spatially averaging observations over the broader metropolitan scale, under which conditions the diurnal variation in NO 2 reflects a dynamic balance between emission and chemical loss. [ABSTRACT FROM AUTHOR]

Surface and satellite observations of atmospheric methane show smooth seasonal behavior in the Southern Hemisphere driven by loss from the hydroxyl (OH) radical. However, observations in the Northern Hemisphere show a sharp mid‐summer increase that is asymmetric with the Southern Hemisphere and not captured by the default configuration of the GEOS‐Chem chemical transport model. Using an ensemble of 22 OH model estimates and 24 wetland emission inventories in GEOS‐Chem, we show that the magnitude, latitudinal distribution, and seasonality of Northern Hemisphere wetland emissions are critical for reproducing the observed seasonality of methane in that hemisphere, with the interhemispheric OH ratio playing a lesser role. Reproducing the observed seasonality requires a wetland emission inventory with ∼80 Tg a−1 poleward of 10°N including significant emissions in South Asia, and an August peak in boreal emissions persisting into autumn. In our 24‐member wetland emission ensemble, only the LPJ‐wsl MERRA‐2 inventory has these attributes. Plain Language Summary: The amount of methane, a powerful greenhouse gas, has been growing in Earth's atmosphere during the last decade, and scientists disagree about which methane sources and sinks are responsible for the growth. One clue into understanding methane's sources and sinks is their seasonality—their month‐to‐month cycles that happen every year. Measurements of atmospheric methane taken at the Earth's surface and using satellite instruments show a steep increase each summer in the Northern Hemisphere that is not replicated when methane is simulated in a global chemical transport model, indicating missing information about source and sink seasonalities. To investigate, we use that model to simulate 24 representations of methane's largest source, emissions from wetlands, and 22 representations of its largest sink, chemical loss by the hydroxyl radical (OH). We find that OH is unlikely to cause the summer increase and model bias, but the amount, spatial distribution, and seasonal cycles of global wetland emissions are the strongest drivers. We suggest that these characteristics are linked to the underlying mechanisms determining wetland area and methane production in wetland models. The results unveil the role of global wetlands in driving methane's seasonality and inform research to analyze methane's long‐term trends. Key Points: Northern Hemisphere atmospheric methane shows a summer increase not replicated by the GEOS‐Chem model with its default sources and sinksThe summer increase's timing and magnitude is determined by the magnitude, seasonality, and spatial distribution of NH wetland emissionsInversions of atmospheric methane observations should use a suitable wetland emission inventory and optimize hemispheric OH concentrations [ABSTRACT FROM AUTHOR]

Current methods for detecting atmospheric plumes and inferring point-source rates from high-resolution satellite imagery are labor-intensive and not scalable with regard to the growing satellite dataset available for methane point sources. Here, we present a two-step algorithm called U-Plume for automated detection and quantification of point sources from satellite imagery. The first step delivers plume detection and delineation (masking) with a U-Net machine learning architecture for image segmentation. The second step quantifies the point-source rate from the masked plume using wind speed information and either a convolutional neural network (CNN) or a physics-based integrated mass enhancement (IME) method. The algorithm can process 62 images (each measuring 128 pixels × 128 pixels) per second on a single 2.6 GHz Intel Core i7-9750H CPU. We train the algorithm using large-eddy simulations of methane plumes superimposed on noisy and variable methane background scenes from the GHGSat-C1 satellite instrument. We introduce the concept of point-source observability, Ops=Q/(UWΔB) , as a single dimensionless number to predict plume detectability and source rate quantification error from an instrument as a function of source rate Q , wind speed U , instrument pixel size W , and instrument-dependent background noise ΔB. We show that Ops can powerfully diagnose the ability of an imaging instrument to observe point sources of a certain magnitude under given conditions. U-Plume successfully detects and masks plumes from sources as small as 100 kgh-1 in GHGSat-C1 images over surfaces with low background noise and successfully handles larger point sources over surfaces with substantial background noise. We find that the IME method for source quantification is unbiased over the full range of source rates, while the CNN method is biased towards the mean of its training range. The total error in source rate quantification is dominated by wind speed at low wind speeds and by the masking algorithm at high wind speeds. A wind speed of 2–4 ms-1 is optimal for detection and quantification of point sources from satellite data. [ABSTRACT FROM AUTHOR]

We quantify 2019 annual mean methane emissions in the contiguous US (CONUS) at 0.25° × 0.3125° resolution by inverse analysis of atmospheric methane columns measured by the Tropospheric Monitoring Instrument (TROPOMI). A gridded version of the US Environmental Protection Agency (EPA) Greenhouse Gas Emissions Inventory (GHGI) serves as the basis for the prior estimate for the inversion. We optimize emissions and quantify observing system information content for an eight-member inversion ensemble through analytical minimization of a Bayesian cost function. We achieve high resolution with a reduced-rank characterization of the observing system that optimally preserves information content. Our optimal (posterior) estimate of anthropogenic emissions in CONUS is 30.9 (30.0–31.8) Tg a -1 , where the values in parentheses give the spread of the ensemble. This is a 13 % increase from the 2023 GHGI estimate for CONUS in 2019. We find emissions for livestock of 10.4 (10.0–10.7) Tg a -1 , for oil and gas of 10.4 (10.1–10.7) Tg a -1 , for coal of 1.5 (1.2–1.9) Tg a -1 , for landfills of 6.9 (6.4–7.5) Tg a -1 , for wastewater of 0.6 (0.5–0.7), and for other anthropogenic sources of 1.1 (1.0–1.2) Tg a -1. The largest increase relative to the GHGI occurs for landfills (51 %), with smaller increases for oil and gas (12 %) and livestock (11 %). These three sectors are responsible for 89 % of posterior anthropogenic emissions in CONUS. The largest decrease (28 %) is for coal. We exploit the high resolution of our inversion to quantify emissions from 70 individual landfills, where we find emissions are on median 77 % larger than the values reported to the EPA's Greenhouse Gas Reporting Program (GHGRP), a key data source for the GHGI. We attribute this underestimate to overestimated recovery efficiencies at landfill gas facilities and to under-accounting of site-specific operational changes and leaks. We also quantify emissions for the 48 individual states in CONUS, which we compare to the GHGI's new state-level inventories and to independent state-produced inventories. Our posterior emissions are on average 27 % larger than the GHGI in the largest 10 methane-producing states, with the biggest upward adjustments in states with large oil and gas emissions, including Texas, New Mexico, Louisiana, and Oklahoma. We also calculate emissions for 95 geographically diverse urban areas in CONUS. Emissions for these urban areas total 6.0 (5.4–6.7) Tg a -1 and are on average 39 (27–52) % larger than a gridded version of the 2023 GHGI, which we attribute to underestimated landfill and gas distribution emissions. [ABSTRACT FROM AUTHOR]

Tropospheric ozone is an air pollutant and a greenhouse gas whose anthropogenic production is limited principally by the supply of nitrogen oxides (NOx) from combustion. Tropospheric ozone in the northern hemisphere has been rising despite the flattening of NOx emissions in recent decades. Here we propose that this sustained increase could result from the photolysis of nitrate particles (pNO3−) to regenerate NOx. Including pNO3− photolysis in the GEOS‐Chem atmospheric chemistry model improves the consistency with ozone observations. Our simulations show that pNO3− concentrations have increased since the 1960s because of rising ammonia and falling SO2 emissions, augmenting the increase in ozone in the northern extratropics by about 50% to better match the observed ozone trend. pNO3− will likely continue to increase through 2050, which would drive a continued increase in ozone even as NOx emissions decrease. More work is needed to better understand the mechanism and rates of pNO3− photolysis. Plain Language Summary: In the troposphere, ozone is an air pollutant and a greenhouse gas. Tropospheric ozone forms from reactions involving carbon monoxide and volatile organic compounds in the presence of nitrogen oxides. Global emissions of nitrogen oxides have been leveling off in the past few decades, yet tropospheric ozone levels have kept on rising. We propose that this rise in ozone could be driven by a growing source of nitrogen oxides from the photolysis of nitrate particles, which have become more abundant due to falling sulfur dioxide and rising ammonia emissions. We find that including nitrate particle photolysis in an atmospheric chemistry model improves its consistency with the observed ozone distribution and trends. Our results point to the importance of considering nitrate particle photolysis for future projections of climate forcing from tropospheric ozone, and the need for further work to reduce the uncertainty in the mechanism and rates of the process. Key Points: Particulate nitrate photolysis improves the consistency of tropospheric ozone in the GEOS‐Chem model with observationsIncrease in particulate nitrate due to falling SO2 and rising NH3 emissions could augment the long‐term increase in tropospheric ozoneBetter characterization of the mechanism and rates of particulate nitrate photolysis is needed [ABSTRACT FROM AUTHOR]

Aviation emissions cause global changes in air quality which have been estimated to result in ∼ 58 000 premature mortalities per year, but this number varies by an order of magnitude between studies. The causes of this uncertainty include differences in the assessment of ozone exposure impacts and in how air quality changes are simulated, as well as the possibility that low-resolution (∼ 400 km) global models may overestimate impacts compared to finer-resolution (∼ 50 km) regional models. We use the GEOS-Chem High-Performance chemistry-transport model at a 50 km global resolution, an order of magnitude finer than recent assessments of the same scope, to quantify the air quality impacts of aviation with a single internally consistent global approach. We find that aviation emissions in 2015 resulted in 21 200 (95 % confidence interval due to health response uncertainty: 19 400–22 900) premature mortalities due to particulate matter exposure and 53 100 (36 000–69 900) due to ozone exposure. Compared to a prior estimate of 6800 ozone-related premature mortalities for 2006 our central estimate is increased by 5.6 times due to the use of updated epidemiological data, which includes the effects of ozone exposure during winter, and by 1.3 times due to increased aviation fuel burn. The use of fine (50 km) resolution increases the estimated impacts on both ozone and particulate-matter-related mortality by a further 20 % compared to coarse-resolution (400 km) global simulation, but an intermediate resolution (100 km) is sufficient to capture 98 % of impacts. This is in part due to the role of aviation-attributable ozone, which is long-lived enough to mix through the Northern Hemisphere and exposure to which causes 2.5 times as much health impact as aviation-attributable PM 2.5. This work shows that the air quality impacts of civil aviation emissions are dominated by the hemisphere-scale response of tropospheric ozone to aviation NO x rather than local changes and that simulations at ∼ 100 km resolution provide similar results to those at a 2 times finer spatial scale. However, the overall quantification of health impacts is sensitive to assumptions regarding the response of human health to exposure, and additional research is needed to reduce uncertainty in the physical response of the atmosphere to aviation emissions. [ABSTRACT FROM AUTHOR]

We updated the chemical mechanism of the GEOS-Chem global 3-D model of atmospheric chemistry to include new recommendations from the NASA Jet Propulsion Laboratory (JPL) chemical kinetics Data Evaluation 19-5 and from the International Union of Pure and Applied Chemistry (IUPAC) and to balance carbon and nitrogen. We examined the impact of these updates on the GEOS-Chem version 14.0.1 simulation. Notable changes include 11 updates to reactions of reactive nitrogen species, resulting in a 7 % net increase in the stratospheric NO x (NO + NO 2) burden; an updated CO + OH rate formula leading to a 2.7 % reduction in total tropospheric CO; adjustments to the rate coefficient and branching ratios of propane + OH, leading to reduced tropospheric propane (- 17 %) and increased acetone (+ 3.5 %) burdens; a 41 % increase in the tropospheric burden of peroxyacetic acid due to a decrease in the rate coefficient for its reaction with OH, further contributing to reductions in peroxyacetyl nitrate (PAN; - 3.8 %) and acetic acid (- 3.4 %); and a number of minor adjustments to halogen radical cycling. Changes to the global tropospheric burdens of other species include - 0.7 % for ozone, + 0.3 % for OH (- 0.4 % for methane lifetime against oxidation by tropospheric OH), + 0.8 % for formaldehyde, and - 1.7 % for NO x. The updated mechanism reflects the current state of the science, including complex chemical dependencies of key atmospheric species on temperature, pressure, and concentrations of other compounds. The improved conservation of carbon and nitrogen will facilitate future studies of their overall atmospheric budgets. [ABSTRACT FROM AUTHOR]

Particulate nitrate is a major component of fine particulate matter (PM2.5). Its formation may be varyingly sensitive to emissions of ammonia (NH3), nitrogen oxides (NOx ≡ NO + NO2), and volatile organic compounds (VOCs), depending on local conditions. Diagnosing these sensitivities is critical for successful air quality management. Here, we show that satellite measurements of tropospheric NH3 and NO2 columns can be used as a quick indicator of the dominant sensitivity regime through the NH3/NO2 column ratio together with the NO2 column. We demonstrate the effectiveness of this indicator with the GEOS‐Chem chemical transport model and define thresholds to separate the different sensitivity regimes. Applying the method to wintertime IASI and OMI observations in East Asia reveals that surface nitrate is dominantly VOC‐sensitive in the southern North China Plain (NCP), NOx‐sensitive in most of the East China Plain, and NH3‐sensitive in the northern NCP, southern China, and Korea. Plain Language Summary: We present a novel application of satellite remote sensing to better understand the causes of particulate nitrate pollution. Particulate nitrate is a major air pollutant throughout the urbanized world. It is produced by atmospheric oxidation of emitted nitrogen oxides (NOx) but may be more sensitive to emissions of ammonia (NH3) or volatile organic compounds (VOCs). Understanding which of NH3, NOx, or VOC emissions is most important in driving nitrate formation is critical for air quality management. We show that satellite measurements of the NH3/NO2 column ratio along with NO2 columns is an effective indicator to determine the dominant sensitivity regime (NH3−,NOx−,orVOC−sensitive ${\text{NH}}_{3}-,{\text{NO}}_{\mathrm{x}}-,\text{or}\,\text{VOC}-\text{sensitive}$). We develop this approach using an atmospheric chemistry model and apply it to wintertime satellite observations in East Asia. The approach should be applicable to other continents, seasons, and a broader range of satellite instruments, providing valuable insights for particulate nitrate reduction strategies. Key Points: Reducing particulate nitrate pollution requires understanding its local sensitivities to NH3, NOx, and volatile organic compound emissionsSatellite observation of the NH3/NO2 column ratio is an effective indicator for diagnosing these sensitivitiesIASI NH3 and OMI NO2 observations reveal varying regimes of nitrate sensitivity across wintertime East Asia [ABSTRACT FROM AUTHOR]

We demonstrate geostationary satellite monitoring of large transient methane point sources with the US Geostationary Operational Environmental Satellites (GOES). GOES provides continuous 5- to 10-min coverage of the Americas at 1 to 2 km nadir pixel resolution in two shortwave infrared spectral bands from which large methane plumes can be retrieved. We track the full evolution of an extreme methane release from the El Encino--La Laguna natural gas pipeline in Durango, Mexico on 12 May 2019. The release lasted 3 h at a variable rate of 260 to 550 metric tons of methane per hour and totaled 1,130 to 1,380 metric tons. We report several other detections of transient point sources from oil/gas infrastructure, from which we infer a detection limit of 10 to 100 t h-1. Our results show that extreme releases of methane can last less than an hour, as from deliberate venting, and would thus be difficult to identify and quantify with low-Earth orbit satellites. [ABSTRACT FROM AUTHOR]

Nitrogen oxide radicals (NOx = NO + NO2) emitted by fuel combustion are important precursors of ozone and particulate matter pollution, and NO2 itself is harmful to public health. The Geostationary Environment Monitoring Spectrometer (GEMS), launched in space in 2020, now provides hourly daytime observations of NO2 columns over East Asia. This diurnal variation offers unique information on the emission and chemistry of NOx, but it needs to be carefully interpreted. Here we investigate the drivers of the diurnal variation of NO2 observed by GEMS during winter and summer over Beijing and Seoul. We place the GEMS observations in the context of ground-based column observations (Pandora instruments) and GEOS-Chem chemical transport model simulations. We find good agreement between the diurnal variations of NO2 columns in GEMS, Pandora, and GEOS-Chem, and we use GEOS-Chem to interpret these variations. NOx emissions are four times higher in the daytime than at night, driving an accumulation of NO2 over the course of the day, offset by losses from chemistry and transport (horizontal flux divergence). For the urban core, where the Pandora instruments are located, we find that NO2 in winter increases throughout the day due to high daytime emissions and increasing NO2/NOx ratio from entrainment of ozone, partly balanced by loss from transport and with negligible role of chemistry. In summer, by contrast, chemical loss combined with transport drives a minimum in the NO2 column at 13-14 local time. Segregation of the GEMS data by wind speed further demonstrates the effect of transport, with NO2 in winter accumulating throughout the day at low winds but flat at high winds. The effect of transport can be minimized in summer by spatially averaging observations over the broader metropolitan scale, under which conditions the diurnal variation of NO2 reflects a dynamic balance between emission and chemical loss. [ABSTRACT FROM AUTHOR]

Nitrogen oxide radicals (NOx ≡ NO + NO2) emitted by fuel combustion are important precursors of ozone and particulate matter pollution, and NO2 itself is harmful to public health. The Geostationary Environment Monitoring Spectrometer (GEMS), launched in space in 2020, now provides hourly daytime observations of NO2 columns over East Asia. This diurnal variation offers unique information on the emission and chemistry of NOx,but it needs to be carefully interpreted. Here we investigate the drivers of the diurnal variation of NO2 observed by GEMS during winter and summer over Beijing and Seoul. We place the GEMS observations in the context of ground-based column observations (Pandora instruments) and GEOS-Chem chemical transport model simulations. We find good agreement between the diurnal variations of NO2 columns in GEMS, Pandora, and GEOS-Chem, and we use GEOS-Chem to interpret these variations. NOx emissions are four times higher in the daytime than at night, driving an accumulation of NO2 over the course of the day, offset by losses from chemistry and transport (horizontal flux divergence). For the urban core, where the Pandora instruments are located, we find that NO2 in winter increases throughout the day due to high daytime emissions and increasing NO2/NOx ratio from entrainment of ozone, partly balanced by loss from transport and with negligible role of chemistry. In summer, by contrast, chemical loss combined with transport drives a minimum in the NO2 column at 13–14 local time. Segregation of the GEMS data by wind speed further demonstrates the effect of transport, with NO2 in winter accumulating throughout the day at low winds but flat at high winds. The effect of transport can be minimized in summer by spatially averaging observations over the broader metropolitan scale, under which conditions the diurnal variation of NO2 reflects a dynamic balance between emission and chemical loss. [ABSTRACT FROM AUTHOR]

Volcanism is the largest natural source of mercury (Hg) to the biosphere. However, past Hg emission estimates have varied by three orders of magnitude. Here, we present an updated central estimate and interquartile range (232 Mg a−1; IQR: 170–336 Mg a−1) for modern volcanic Hg emissions based on advances in satellite remote sensing of sulfur dioxide (SO2) and an improved method for considering uncertainty in Hg:SO2 emissions ratios. Atmospheric modeling shows the influence of volcanic Hg on surface atmospheric concentrations in the extratropical Northern Hemisphere is 1.8 times higher than in the Southern Hemisphere. Spatiotemporal variability in volcanic Hg emissions may obscure atmospheric trends forced by anthropogenic emissions at some locations. This should be considered when selecting monitoring sites to inform global regulatory actions. Volcanic emission estimates from this work suggest the pre‐anthropogenic global atmospheric Hg reservoir was 580 Mg, 7‐fold lower than in 2015 (4,000 Mg). Plain Language Summary: Volcanism is widely recognized as the most important natural source of mercury (Hg) globally, but existing emissions estimates contain substantial uncertainty. This study combines satellite observations of sulfur dioxide (SO2) in volcanic plumes and measured Hg:SO2 ratios to quantify the magnitude and spatiotemporal variability of global volcanic Hg emissions. Using a global model, we show that the spatial pattern of volcanic releases and atmospheric dynamics result in greater concentrations of volcanic Hg in the mid‐latitude Northern Hemisphere compared to the mid‐latitude Southern Hemisphere. Modeling results suggest that variability in volcanic Hg emissions at some locations may obscure trends in atmospheric Hg concentrations driven by human emissions. The influence of volcanic Hg emissions should therefore be considered during selection of global monitoring sites used to track the progress of regulatory actions designed to mitigate Hg pollution. Volcanic release estimates from this work suggest the natural atmospheric Hg reservoir was ∼7 times smaller than in 2015, reinforcing that humans have profoundly disrupted the global biogeochemical Hg cycle. Key Points: Volcanic mercury emissions of 232 Mg a−1 (IQR: 170–336 Mg a−1) are estimated by indexing to sulfur dioxide from satellite remote sensingOver 90% of volcanic mercury emissions occur in the tropics and mid‐latitude Northern HemisphereVolcanic emissions support a pre‐anthropogenic atmospheric mercury reservoir of approximately 580 Mg (7‐fold lower than in 2015) [ABSTRACT FROM AUTHOR]

Current methods for detecting atmospheric plumes and inferring point source rates from high-resolution satellite imagery are labor intensive and not scalable to the growing satellite dataset available for methane point sources. Here we present a two-step algorithm called U-Plume for automated detection and quantification of point sources from satellite imagery. The first step delivers plume detection and delineation (masking) with a machine learning U-Net architecture for image segmentation. The second step quantifies point source rate from the masked plume using wind speed information and either a convolution neural network (CNN) or a physics-based Integrated Mass Enhancement (IME) method. The algorithm can process 62 128×128 images per second on a single core. We train the algorithm with large-eddy simulations of methane plumes superimposed on noisy and variable methane background scenes from the GHGSat-C1 satellite instrument. We introduce the concept of point source observability Ops = Q/(UW?B) as a single dimensionless number to predict plume detectability and source rate quantification error from an instrument as a function of source rate Q, wind speed U, instrument pixel size W, and instrument-dependent background noise κB. We show that Ops can powerfully diagnose the ability of an imaging instrument to observe point sources of a certain magnitude under given conditions. U-Plume successfully detects and masks plumes from sources as small as 100 kg h-1 over surfaces with low background noise and succeeds for larger point sources over surfaces with substantial background noise. We find that the IME method for source quantification is unbiased over the full range of source rates while the CNN method is biased toward the mean of its training range. The total error in source rate quantification is dominated by wind speed at low wind speeds and by the masking algorithm at high wind speeds. A wind speed of 2-4 m s-1 is optimal for detection and quantification of point sources from satellite data. [ABSTRACT FROM AUTHOR]

Satellite observations of dry-column methane mixing ratios (XCH 4) from shortwave infrared (SWIR) solar backscatter radiation provide a powerful resource to quantify methane emissions in service of climate action. The TROPOspheric Monitoring Instrument (TROPOMI), launched in October 2017, provides global daily coverage at a 5.5 × 7 km 2 (nadir) pixel resolution, but its methane retrievals can suffer from biases associated with SWIR surface albedo, scattering from aerosols and cirrus clouds, and across-track variability (striping). The Greenhouse gases Observing SATellite (GOSAT) instrument, launched in 2009, has better spectral characteristics and its methane retrieval is much less subject to biases, but its data density is 250 times sparser than TROPOMI. Here, we present a blended TROPOMI + GOSAT methane product obtained by training a machine learning (ML) model to predict the difference between TROPOMI and GOSAT co-located measurements, using only predictor variables included in the TROPOMI retrieval, and then applying the correction to the complete TROPOMI record from April 2018 to present. We find that the largest corrections are associated with coarse aerosol particles, high SWIR surface albedo, and across-track pixel index. Our blended product corrects a systematic difference between TROPOMI and GOSAT over water, and it features corrections exceeding 10 ppb over arid land, persistently cloudy regions, and high northern latitudes. It reduces the TROPOMI spatially variable bias over land (referenced to GOSAT data) from 14.3 to 10.4 ppb at a 0.25 ∘ × 0.3125 ∘ resolution. Validation with Total Carbon Column Observing Network (TCCON) ground-based column measurements shows reductions in variable bias compared with the original TROPOMI data from 4.7 to 4.4 ppb and in single-retrieval precision from 14.5 to 11.9 ppb. TCCON data are all in locations with a SWIR surface albedo below 0.4 (where TROPOMI biases tend to be relatively low), but they confirm the dependence of TROPOMI biases on SWIR surface albedo and coarse aerosol particles, as well as the reduction of these biases in the blended product. Fine-scale inspection of the Arabian Peninsula shows that a number of hotspots in the original TROPOMI data are removed as artifacts in the blended product. The blended product also corrects striping and aerosol/cloud biases in single-orbit TROPOMI data, enabling better detection and quantification of ultra-emitters. Residual coastal biases can be removed by applying additional filters. The ML method presented here can be applied more generally to validate and correct data from any new satellite instrument by reference to a more established instrument. [ABSTRACT FROM AUTHOR]

We present a versatile, powerful, and user-friendly chemical data assimilation toolkit for simultaneously optimizing emissions and concentrations of chemical species based on atmospheric observations from satellites or suborbital platforms. The CHemistry and Emissions REanalysis Interface with Observations (CHEEREIO) exploits the GEOS-Chem chemical transport model and a localized ensemble transform Kalman filter algorithm (LETKF) to determine the Bayesian optimal (posterior) emissions and/or concentrations of a set of species based on observations and prior information using an easy-to-modify configuration file with minimal changes to the GEOS-Chem or LETKF code base. The LETKF algorithm readily allows for nonlinear chemistry and produces flow-dependent posterior error covariances from the ensemble simulation spread. The object-oriented Python-based design of CHEEREIO allows users to easily add new observation operators such as for satellites. CHEEREIO takes advantage of the Harmonized Emissions Component (HEMCO) modular structure of input data management in GEOS-Chem to update emissions from the assimilation process independently from the GEOS-Chem code. It can seamlessly support GEOS-Chem version updates and is adaptable to other chemical transport models with similar modular input data structure. A post-processing suite combines ensemble output into consolidated NetCDF files and supports a wide variety of diagnostic data and visualizations. We demonstrate CHEEREIO's capabilities with an out-of-the-box application, assimilating global methane emissions and concentrations at weekly temporal resolution and 2 ∘ × 2.5 ∘ spatial resolution for 2019 using TROPOspheric Monitoring Instrument (TROPOMI) satellite observations. CHEEREIO achieves a 50-fold improvement in computational performance compared to the equivalent analytical inversion of TROPOMI observations. [ABSTRACT FROM AUTHOR]