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National parks in the United States are protected areas wherein the natural habitat is to be conserved for future generations. Deposition of anthropogenic nitrogen (N) transported from areas of human activity (fuel combustion, agriculture) may affect these natural habitats if it exceeds an ecosystem-dependent critical load (CL). We quantify and interpret the deposition to Class I US national parks for present-day and future (2050) conditions using the GEOS-Chem global chemical transport model with 1/2° × 2/3° horizontal resolution over North America. We estimate CL values in the range 2.5–5 kg N ha−1 yr−1 for the different parks to protect the most sensitive ecosystem receptors. For present-day conditions, we find 24 out of 45 parks to be in CL exceedance and 14 more to be marginally so. Many of these are in remote areas of the West. Most (40–85%) of the deposition originates from NOx emissions (fuel combustion). We project future changes in N deposition using representative concentration pathway (RCP) anthropogenic emission scenarios for 2050. These feature 52–73% declines in US NOx emissions relative to present but 19–50% increases in US ammonia (NH3) emissions. Nitrogen deposition at US national parks then becomes dominated by domestic NH3 emissions. While deposition decreases in the East relative to present, there is little progress in the West and increases in some regions. We find that 17–25 US national parks will have CL exceedances in 2050 based on the RCP8.5 and RCP2.6 scenarios. Even in total absence of anthropogenic NOx emissions, 14–18 parks would still have a CL exceedance. Returning all parks to N deposition below CL by 2050 would require at least a 50% decrease in US anthropogenic NH3 emissions relative to RCP-projected 2050 levels., Engineering and Applied Sciences

The article discusses that Methane is a potent greenhouse gas with a relatively short average atmospheric residence time of about a decade and is also a precursor of tropospheric ozone; and mentions the rapid increase in oil and natural gas (O/G) production in the U.S. since around 2005, driven primarily by hydraulic fracturing and horizontal drilling, has led to major concerns about increasing methane emissions and adverse climate impacts.

Cloud computing platforms can provide fast and easy access to complex Earth science models and large datasets. This article presents a mature capability for running the GEOS-Chem global 3D model of atmospheric chemistry on the Amazon Web Services (AWS) cloud. GEOS-Chem users at any experience level can get immediate access to the latest, standard version of the model in a preconfigured software environment with all needed meteorological and other input data, and they can analyze model output data easily within the cloud using Python tools in Jupyter notebooks. Users with no prior knowledge of cloud computing are provided with easy-to-follow, step-by-step instructions. They can learn how to complete a demo project in less than one hour, and from there they can configure and submit their own simulations. The cloud is particularly attractive for beginning and occasional users who otherwise may need to spend substantial time configuring a local computing environment. Heavy users with their own local clusters can also benefit from the cloud to access the latest standard model and datasets, share simulation configurations and results, benchmark local simulations, and respond to surges in computing demand. Software containers allow GEOS-Chem and its software environment to be moved smoothly between cloud platforms and local clusters, so that the exact same simulation can be reproduced everywhere. Because the software requirements and workflows tend to be similar across Earth science models, the work presented here provides general guidance for porting models to cloud computing platforms in a user-accessible way. [ABSTRACT FROM AUTHOR]

The global budget of tropospheric ozone is governed by a complicated ensemble of coupled chemical and dynamical processes. Simulation of tropospheric ozone has been a major focus of the GEOS-Chem chemical transport model (CTM) over the past 20 years, and many developments over the years have affected the model representation of the ozone budget. Here we conduct a comprehensive evaluation of the standard version of GEOS-Chem (v10-01) with ozone observations from ozonesondes, the OMI satellite instrument, and MOZAIC-IAGOS commercial aircraft for 2012–2013. Global validation of the OMI 700-400 hPa data with ozonesondes shows that OMI maintained persistent high quality and no significant drift over the 2006–2013 period. GEOS-Chem shows no significant seasonal or latitudinal bias relative to OMI and strong correlations in all seasons on the 2° × 2.5° horizontal scale ( r = 0.88–0.95), improving on previous model versions. The most pronounced model bias revealed by ozonesondes and MOZAIC-IAGOS is at high northern latitudes in winter-spring where the model is 10–20 ppbv too low. This appears to be due to insufficient stratosphere-troposphere exchange (STE). Model updates to lightning NO x , Asian anthropogenic emissions, bromine chemistry, isoprene chemistry, and meteorological fields over the past decade have overall led to gradual increase in the simulated global tropospheric ozone burden and more active ozone production and loss. From simulations with different versions of GEOS meteorological fields we find that tropospheric ozone in GEOS-Chem v10-01 has a global production rate of 4960–5530 Tg a −1 , lifetime of 20.9–24.2 days, burden of 345–357 Tg, and STE of 325–492 Tg a −1 . Change in the intensity of tropical deep convection between these different meteorological fields is a major factor driving differences in the ozone budget. [ABSTRACT FROM AUTHOR]

We use satellite observations from the Tropospheric Monitoring Instrument (TROPOMI) to map and quantify methane emissions from eastern Mexico using an atmospheric inverse analysis. Eastern Mexico covers the vast majority of the national oil and gas production. Using TROPOMI measurements from May 2018 to December 2019, our methane emission estimates for eastern Mexico are 5.0 ± 0.2 Tg a−1 for anthropogenic sources and 1.5 ± 0.1 Tg a−1 for natural sources, representing 45% and 34% higher annual methane fluxes respectively compared to the most recent estimates based on the Mexican national greenhouse gas inventory. Our results show that Mexico's oil and gas sector has the largest discrepancy, with oil and gas emissions (1.3 ± 0.2 Tg a−1) higher by a factor of two relative to bottom-up estimates—accounting for a quarter of total anthropogenic emissions. Our satellite-based inverse modeling estimates show that more than half of the oil/gas emissions in eastern Mexico are from the southern onshore basin (0.79 ± 0.13 Tg a−1), pointing at high emission sources which are not represented in current bottom-up inventories (e.g., venting of associated gas, high-emitting gathering/processing facilities related to the transport of associated gas from offshore). These findings suggest that stronger mitigation measures are critical to curbing the anthropogenic footprint of methane emissions in Mexico, especially the large contribution from the oil and gas sector. • We quantify regional methane emissions using inverse analysis of TROPOMI data • Mexico's anthropogenic methane emissions are 45% higher than bottom-up estimates • Mexico's oil and gas methane emissions are >2 times higher than bottom-up estimates • Stronger mitigation measures are needed to curb Mexico's methane emissions [ABSTRACT FROM AUTHOR]

The burning of fossil fuels – especially coal, petrol, and diesel – is a major source of airborne fine particulate matter (PM 2.5), and a key contributor to the global burden of mortality and disease. Previous risk assessments have examined the health response to total PM 2.5 , not just PM 2.5 from fossil fuel combustion, and have used a concentration-response function with limited support from the literature and data at both high and low concentrations. This assessment examines mortality associated with PM 2.5 from only fossil fuel combustion, making use of a recent meta-analysis of newer studies with a wider range of exposure. We also estimated mortality due to lower respiratory infections (LRI) among children under the age of five in the Americas and Europe, regions for which we have reliable data on the relative risk of this health outcome from PM 2.5 exposure. We used the chemical transport model GEOS-Chem to estimate global exposure levels to fossil-fuel related PM 2.5 in 2012. Relative risks of mortality were modeled using functions that link long-term exposure to PM 2.5 and mortality, incorporating nonlinearity in the concentration response. We estimate a global total of 10.2 (95% CI: −47.1 to 17.0) million premature deaths annually attributable to the fossil-fuel component of PM 2.5. The greatest mortality impact is estimated over regions with substantial fossil fuel related PM 2.5 , notably China (3.9 million), India (2.5 million) and parts of eastern US, Europe and Southeast Asia. The estimate for China predates substantial decline in fossil fuel emissions and decreases to 2.4 million premature deaths due to 43.7% reduction in fossil fuel PM 2.5 from 2012 to 2018 bringing the global total to 8.7 (95% CI: −1.8 to 14.0) million premature deaths. We also estimated excess annual deaths due to LRI in children (0–4 years old) of 876 in North America, 747 in South America, and 605 in Europe. This study demonstrates that the fossil fuel component of PM 2.5 contributes a large mortality burden. The steeper concentration-response function slope at lower concentrations leads to larger estimates than previously found in Europe and North America, and the slower drop-off in slope at higher concentrations results in larger estimates in Asia. Fossil fuel combustion can be more readily controlled than other sources and precursors of PM 2.5 such as dust or wildfire smoke, so this is a clear message to policymakers and stakeholders to further incentivize a shift to clean sources of energy. • Fossil fuel combustion emits particulate matter (PM 2.5) harmful to public health. • We use a re-evaluated concentration-response function (CRF). • We estimate 10.2 million global excess deaths in 2012 due to PM 2.5 from this source. • 62% of deaths are in China (3.9 million) and India (2.5 million). • Our estimate is more than double the GBD reports, due to the updated CRF we use. [ABSTRACT FROM AUTHOR]

Peroxyacetyl nitrate (PAN) is produced in the atmosphere by photochemical oxidation of non-methane volatile organic compounds in the presence of nitrogen oxides (NO x ), and it can be transported over long distances at cold temperatures before decomposing thermally to release NO x in the remote troposphere. It is both a tracer and a precursor for transpacific ozone pollution transported from East Asia to North America. Here, we directly demonstrate this transport with PAN satellite observations from the infrared atmospheric sounding interferometer (IASI). We reprocess the IASI PAN retrievals by replacing the constant prior vertical profile with vertical shape factors from the GEOS-Chem model that capture the contrasting shapes observed from aircraft over South Korea (KORUS-AQ) and the North Pacific (ATom). The reprocessed IASI PAN observations show maximum transpacific transport of East Asian pollution in spring, with events over the Northeast Pacific offshore from the Western US associated in GEOS-Chem with elevated ozone in the lower free troposphere. However, these events increase surface ozone in the US by less than 1 ppbv because the East Asian pollution mainly remains offshore as it circulates the Pacific High.

Nationally reported greenhouse gas inventories are a core component of the Paris Agreement's transparency framework. Comparisons with emission estimates derived from atmospheric observations help identify improvements to reduce uncertainties and increase the confidence in reported values. To facilitate comparisons over the contiguous United States, we present a 0.1° × 0.1° gridded inventory of annual 2012-2018 anthropogenic methane emissions, allocated to 26 individual source categories, with scale-dependent error estimates. Our inventory is consistent with the U.S. Environmental Protection Agency (EPA) Inventory of U.S. Greenhouse Gas Emissions and Sinks (GHGI), submitted to the United Nations in 2020. Total emissions and patterns (spatial/temporal) reflect the activity and emission factor data underlying the GHGI, including many updates relative to a previous gridded version of the GHGI that has been extensively compared with observations. These underlying data are not generally available in global gridded inventories, and comparison to EDGAR version 6 shows large spatial differences, particularly for the oil and gas sectors. We also find strong regional variability across all sources in annual 2012-2018 spatial trends, highlighting the importance of understanding regional- and facility-level activities. Our inventory represents the first time series of gridded GHGI methane emissions and enables robust comparisons of emissions and their trends with atmospheric observations.

The United States is the world's largest oil/gas methane emitter according to current national reports. Reducing these emissions is a top priority in the US government's climate action plan. Here, we use a 2010 to 2019 high-resolution inversion of surface and satellite observations of atmospheric methane to quantify emission trends for individual oil/gas production regions in North America and relate them to production and infrastructure. We estimate a mean US oil/gas methane emission of 14.8 (12.4 to 16.5) Tg a -1 for 2010 to 2019, 70% higher than reported by the US Environmental Protection Agency. While emissions in Canada and Mexico decreased over the period, US emissions increased from 2010 to 2014, decreased until 2017, and rose again afterward. Increases were driven by the largest production regions (Permian, Anadarko, Marcellus), while emissions in the smaller production regions generally decreased. Much of the year-to-year emission variability can be explained by oil/gas production rates, active well counts, and new wells drilled, with the 2014 to 2017 decrease driven by reduction in new wells and the 2017 to 2019 surge driven by upswing of production. We find a steady decrease in the oil/gas methane intensity (emission per unit methane gas production) for almost all major US production regions. The mean US methane intensity decreased from 3.7% in 2010 to 2.5% in 2019. If the methane intensity for the oil/gas supply chain continues to decrease at this pace, we may expect a 32% decrease in US oil/gas emissions by 2030 despite projected increases in production.

Background: The health impacts of wildfire smoke, including fine particles (PM2.5), are not well understood and may differ from those of PM2.5 from other sources due to differences in concentrations and chemical composition., Methods: First, for the entire Western United States (561 counties) for 2004-2009, we estimated daily PM2.5 concentrations directly attributable to wildfires (wildfires-specific PM2.5), using a global chemical transport model. Second, we defined smoke wave as ≥2 consecutive days with daily wildfire-specific PM2.5 > 20 μg/m, with sensitivity analysis considering 23, 28, and 37 μg/m. Third, we estimated the risk of cardiovascular and respiratory hospital admissions associated with smoke waves for Medicare enrollees. We used a generalized linear mixed model to estimate the relative risk of hospital admissions on smoke wave days compared with matched comparison days without wildfire smoke., Results: We estimated that about 46 million people of all ages were exposed to at least one smoke wave during 2004 to 2009 in the Western United States. Of these, 5 million are Medicare enrollees (≥65 years). We found a 7.2% (95% confidence interval: 0.25%, 15%) increase in risk of respiratory admissions during smoke wave days with high wildfire-specific PM2.5 (>37 μg/m) compared with matched non smoke wave days. We did not observe an association between smoke wave days with wildfire-specific PM2.5 ≤ 37 μg/mand respiratory or cardiovascular admissions. Respiratory effects of wildfire-specific PM2.5 may be stronger than that of PM2.5 from other sources., Conclusion: Short-term exposure to wildfire-specific PM2.5was associated with risk of respiratory diseases in the elderly population in the Western United States during severe smoke days. See video abstract at, http://links.lww.com/EDE/B137., Competing Interests: none to declare

We present a gridded inventory of US anthropogenic methane emissions with 0.1° × 0.1° spatial resolution, monthly temporal resolution, and detailed scale-dependent error characterization. The inventory is designed to be consistent with the 2016 US Environmental Protection Agency (EPA) Inventory of US Greenhouse Gas Emissions and Sinks (GHGI) for 2012. The EPA inventory is available only as national totals for different source types. We use a wide range of databases at the state, county, local, and point source level to disaggregate the inventory and allocate the spatial and temporal distribution of emissions for individual source types. Results show large differences with the EDGAR v4.2 global gridded inventory commonly used as a priori estimate in inversions of atmospheric methane observations. We derive grid-dependent error statistics for individual source types from comparison with the Environmental Defense Fund (EDF) regional inventory for Northeast Texas. These error statistics are independently verified by comparison with the California Greenhouse Gas Emissions Measurement (CALGEM) grid-resolved emission inventory. Our gridded, time-resolved inventory provides an improved basis for inversion of atmospheric methane observations to estimate US methane emissions and interpret the results in terms of the underlying processes.

Wildfire can impose a direct impact on human health under climate change. While the potential impacts of climate change on wildfires and resulting air pollution have been studied, it is not known who will be most affected by the growing threat of wildfires. Identifying communities that will be most affected will inform development of fire management strategies and disaster preparedness programs. We estimate levels of fine particulate matter (PM 2.5 ) directly attributable to wildfires in 561 western US counties during fire seasons for the present-day (2004-2009) and future (2046-2051), using a fire prediction model and GEOS-Chem, a 3-D global chemical transport model. Future estimates are obtained under a scenario of moderately increasing greenhouse gases by mid-century. We create a new term "Smoke Wave," defined as ≥2 consecutive days with high wildfire-specific PM 2.5 , to describe episodes of high air pollution from wildfires. We develop an interactive map to demonstrate the counties likely to suffer from future high wildfire pollution events. For 2004-2009, on days exceeding regulatory PM 2.5 standards, wildfires contributed an average of 71.3% of total PM 2.5 . Under future climate change, we estimate that more than 82 million individuals will experience a 57% and 31% increase in the frequency and intensity, respectively, of Smoke Waves. Northern California, Western Oregon and the Great Plains are likely to suffer the highest exposure to widlfire smoke in the future. Results point to the potential health impacts of increasing wildfire activity on large numbers of people in a warming climate and the need to establish or modify US wildfire management and evacuation programs in high-risk regions. The study also adds to the growing literature arguing that extreme events in a changing climate could have significant consequences for human health.

Formaldehyde (HCHO) column data from satellites are widely used as a proxy for emissions of volatile organic compounds (VOCs) but validation of the data has been extremely limited. Here we use highly accurate HCHO aircraft observations from the NASA SEAC 4 RS campaign over the Southeast US in August-September 2013 to validate and intercompare six retrievals of HCHO columns from four different satellite instruments (OMI, GOME2A, GOME2B and OMPS) and three different research groups. The GEOS-Chem chemical transport model is used as a common intercomparison platform. All retrievals feature a HCHO maximum over Arkansas and Louisiana, consistent with the aircraft observations and reflecting high emissions of biogenic isoprene. The retrievals are also interconsistent in their spatial variability over the Southeast US ( r =0.4-0.8 on a 0.5°×0.5° grid) and in their day-to-day variability ( r =0.5-0.8). However, all retrievals are biased low in the mean by 20-51%, which would lead to corresponding bias in estimates of isoprene emissions from the satellite data. The smallest bias is for OMI-BIRA, which has high corrected slant columns relative to the other retrievals and low scattering weights in its air mass factor ( AMF ) calculation. OMI-BIRA has systematic error in its assumed vertical HCHO shape profiles for the AMF calculation and correcting this would eliminate its bias relative to the SEAC 4 RS data. Our results support the use of satellite HCHO data as a quantitative proxy for isoprene emission after correction of the low mean bias. There is no evident pattern in the bias, suggesting that a uniform correction factor may be applied to the data until better understanding is achieved.

Ozone pollution in the Southeast US involves complex chemistry driven by emissions of anthropogenic nitrogen oxide radicals (NO x ≡ NO + NO 2 ) 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 SEAC 4 RS 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 NO x from the US Environmental Protection Agency (EPA) is too high. This finding is based on SEAC 4 RS observations of NO x and its oxidation products, surface network observations of nitrate wet deposition fluxes, and OMI satellite observations of tropospheric NO 2 columns. Our results indicate that NEI NO x emissions from mobile and industrial sources must be reduced by 30-60%, dependent on the assumption of the contribution by soil NO x emissions. Upper tropospheric NO 2 from lightning makes a large contribution to satellite observations of tropospheric NO 2 that must be accounted for when using these data to estimate surface NO x emissions. We find that only half of isoprene oxidation proceeds by the high-NO x pathway to produce ozone; this fraction is only moderately sensitive to changes in NO x emissions because isoprene and NO x emissions are spatially segregated. GEOS-Chem with reduced NO x 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 NO x 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.