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Atmospheric chemistry models generally assume organic aerosol (OA) to be photochemically inert. Recent mechanisms for the oxidation of biogenic isoprene, a major source of secondary organic aerosol (iSOA), produce excessive OA in the absence of subsequent OA reactivity. At the same time, models underestimate atmospheric concentrations of formic and acetic acids for which OA degradation could provide a source. Here we show that the aqueous photooxidation of an isoprene-derived organosulfate (2-methyltriolsulfate or MTS), an important iSOA component, produces formic and acetic acids in high yields and at timescales competitive with deposition. Experimental data are well fit by a kinetic model in which three sequential oxidation reactions of the isoprene organosulfate produce two molar equivalents of formic acid and one of acetic acid. We incorporate this chemistry and that of 2-methyltetrol, another ubiquitous iSOA component, into the GEOS-Chem global atmospheric chemistry model. Simulations show that photooxidation and subsequent revolatilization of this iSOA may account for up to half of total iSOA loss globally, producing 4 Tg a−1 each of formic and acetic acids. This reduces model biases in gas-phase formic acid and total organic aerosol over the Southeast United States in summer by ∼30% and 60% respectively. While our study shows the importance of adding iSOA photochemical sinks into atmospheric models, uncertainties remain that warrant further study. In particular, improved understanding of reaction dependencies on particle characteristics and concentrations of particle-phase OH and other oxidants are needed to better simulate the effects of this chemistry on the atmospheric budgets of organic acids and iSOA.

Secondary organic aerosol (SOA) produced by atmospheric oxidation of primary emitted precursors is a major contributor to fine particulate matter (PM2.5) air pollution worldwide. Observations during winter haze pollution episodes in urban China show that most of this SOA originates from fossil-fuel combustion but the chemical mechanisms involved are unclear. Here we report field observations in a Beijing winter haze event that reveal fast aqueous-phase conversion of fossil-fuel primary organic aerosol (POA) to SOA at high relative humidity. Analyses of aerosol mass spectra and elemental ratios indicate that ring-breaking oxidation of POA aromatic species, leading to functionalization as carbonyls and carboxylic acids, may serve as the dominant mechanism for this SOA formation. A POA origin for SOA could explain why SOA has been decreasing over the 2013-2018 period in response to POA emission controls even as emissions of volatile organic compounds (VOCs) have remained flat.

Global high-resolution observations of atmospheric composition from satellites can greatly improve our understanding of surface emissions through inverse analyses. Variational inverse methods can optimize surface emissions at any resolution but do not readily quantify the error and information content of the posterior solution. The information content of satellite data may be much lower than its coverage would suggest because of failed retrievals, instrument noise, and error correlations that propagate through the inversion. Analytical solution of the inverse problem provides closed-form characterization of posterior error statistics and information content but requires the construction of the Jacobian matrix that relates emissions to atmospheric concentrations. Building the Jacobian matrix is computationally expensive at high resolution because it involves perturbing each emission element, typically individual grid cells, in the atmospheric transport model used as the forward model for the inversion. We propose and analyze two methods, reduced dimension and reduced rank, to construct the Jacobian matrix at greatly decreased computational cost while retaining information content. Both methods are two-step iterative procedures that begin from an initial native-resolution estimate of the Jacobian matrix constructed at no computational cost by assuming that atmospheric concentrations are most sensitive to local emissions. The reduced-dimension method uses this estimate to construct a Jacobian matrix on a multiscale grid that maintains a high resolution in areas with high information content and aggregates grid cells elsewhere. The reduced-rank method constructs the Jacobian matrix at native resolution by perturbing the leading patterns of information content given by the initial estimate. We demonstrate both methods in an analytical Bayesian inversion of Greenhouse Gases Observing Satellite (GOSAT) methane data with augmented information content over North America in July 2009. We show that both methods reproduce the results of the native-resolution inversion while achieving a factor of 4 improvement in computational performance. The reduced-dimension method produces an exact solution at a lower spatial resolution, while the reduced-rank method solves the inversion at native resolution in areas of high information content and defaults to the prior estimate elsewhere.

Severe events of wintertime particulate air pollution in Beijing (winter haze) are associated with high relative humidity (RH) and fast production of particulate sulfate from the oxidation of sulfur dioxide (SO2) emitted by coal combustion. There has been considerable debate regarding the mechanism for SO2 oxidation. Here we show evidence from field observations of a haze event that rapid oxidation of SO2 by nitrogen dioxide (NO2) and nitrous acid (HONO) takes place, the latter producing nitrous oxide (N2O). Sulfate shifts to larger particle sizes during the event, indicative of fog/cloud processing. Fog and cloud readily form under winter haze conditions, leading to high liquid water contents with high pH (>5.5) from elevated ammonia. Such conditions enable fast aqueous-phase oxidation of SO2 by NO2, producing HONO which can in turn oxidize SO2 to yield N2O.This mechanism could provide an explanation for sulfate formation under some winter haze conditions.

Emissions of particulate matter from fires associated with land management practices in Indonesia contribute to regional air pollution and mortality. We assess the public health benefits in Indonesia, Malaysia, and Singapore from policies to reduce fires by integrating information on fire emissions, atmospheric transport patterns, and population exposure to fine particulate matter (PM2.5). We use adjoint sensitivities to relate fire emissions to PM2.5 for a range of meteorological conditions and find that a Business-As-Usual scenario of land use change leads, on average, to 36,000 excess deaths per year into the foreseeable future (the next several decades) across the region. These deaths are largely preventable with fire reduction strategies, such as blocking fires in peatlands, industrial concessions, or protected areas, which reduce the health burden by 66, 45, and 14%, respectively. The effectiveness of these different strategies in mitigating human health impacts depends on the location of fires relative to the population distribution. For example, protecting peatlands through eliminating all fires on such lands would prevent on average 24,000 excess deaths per year into the foreseeable future across the region because, in addition to storing large amounts of fuel, many peatlands are located directly upwind of densely populated areas. We also demonstrate how this framework can be used to prioritize restoration locations for the Indonesian Peatland Restoration Agency based on their ability to reduce pollution exposure and health burden. This scientific framework is publicly available through an online decision support tool that allows stakeholders to readily determine the public health benefits of different land management strategies.

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]

Ozone pollution in the Southeast US involves complex chemistry driven by emissions of anthropogenic nitrogen oxide radicals (NOx ≡ NO + NO2) and biogenic isoprene. Model estimates of surface ozone concentrations tend to be biased high in the region and this is of concern for designing effective emission control strategies to meet air quality standards. We use detailed chemical observations from the SEAC4RS aircraft campaign in August and September 2013, interpreted with the GEOS-Chem chemical transport model at 0.25°×0.3125° horizontal resolution, to better understand the factors controlling surface ozone in the Southeast US. We find that the National Emission Inventory (NEI) for NOx from the US Environmental Protection Agency (EPA) is too high. This finding is based on SEAC4RS observations of NOx and its oxidation products, surface network observations of nitrate wet deposition fluxes, and OMI satellite observations of tropospheric NO2 columns. Our results indicate that NEI NOx emissions from mobile and industrial sources must be reduced by 30-60%, dependent on the assumption of the contribution by soil NOx emissions. Upper tropospheric NO2 from lightning makes a large contribution to satellite observations of tropospheric NO2 that must be accounted for when using these data to estimate surface NOx emissions. We find that only half of isoprene oxidation proceeds by the high-NOx pathway to produce ozone; this fraction is only moderately sensitive to changes in NOx emissions because isoprene and NOx emissions are spatially segregated. GEOS-Chem with reduced NOx emissions provides an unbiased simulation of ozone observations from the aircraft, and reproduces the observed ozone production efficiency in the boundary layer as derived from a regression of ozone and NOx oxidation products. However, the model is still biased high by 8±13 ppb relative to observed surface ozone in the Southeast US. Ozonesondes launched during midday hours show a 7 ppb ozone decrease from 1.5 km to the surface that GEOS-Chem does not capture. This bias may reflect a combination of excessive vertical mixing and net ozone production in the model boundary layer.

We estimate methane emissions from North America with high spatial resolution by inversion of Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) satellite observations using the Goddard Earth Observing System Chemistry (GEOS-Chem) chemical transport model and its adjoint. The inversion focuses on summer 2004 when data from the Intercontinental Chemical Transport Experiment-North America (INTEX-A) aircraft campaign over the eastern U.S. are available to validate the SCIAMACHY retrievals and evaluate the inversion. From the INTEX-A data we identify and correct a water vapor-dependent bias in the SCIAMACHY data. We conduct an initial inversion of emissions on the horizontal grid of GEOS-Chem (1/2° × 2/3°) to identify correction tendencies relative to the Emission Database for Global Atmospheric Research (EDGAR) v4.2 emission inventory used as a priori. We then cluster these grid cells with a hierarchical algorithm to extract the maximum information from the SCIAMACHY observations. A 1000 cluster ensemble can be adequately constrained, providing ~100 km resolution across North America. Analysis of results indicates that the Hudson Bay Lowland wetlands source is 2.1Tga-1, lower than the a priori but consistent with other recent estimates. Anthropogenic U.S. emissions are 30.1 ± 1.3 Tg a-1, compared to 25.8Tga-1 and 28.3 Tg a-1 in the EDGAR v4.2 and Environmental Protection Agency (EPA) inventories, respectively. We find that U.S. livestock emissions are 40% greater than in these two inventories. No such discrepancy is apparent for overall U.S. oil and gas emissions, although this may reflect some compensation between overestimate of emissions from storage/distribution and underestimate from production. We find that U.S. livestock emissions are 70% greater than the oil and gas emissions, in contrast to the EDGAR v4.2 and EPA inventories where these two sources are of comparable magnitude.

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]

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]