U.S. Ethane Emissions and Trends Estimated from Atmospheric Observations

Oil and natural gas (O&G) production and processing activities have changed markedly across the U.S. over the past several years. However, the impacts of these changes on air pollution and greenhouse gas emissions are not clear. In this study, we examine U.S. ethane (C2H6) emissions, which are primarily from O&G activities, during years 2015-2020. We use C2H6 observations made by the NOAA Global Monitoring Laboratory and partner organizations from towers and aircraft and estimate emissions from these observations by using an inverse model. We find that U.S. C2H6 emissions (4.43 ± 0.2 Tg·yr-1) are approximately three times those estimated by the EPA's 2017 National Emissions Inventory (NEI) platform (1.54 Tg·yr-1) and exhibit a very different seasonal cycle. We also find that changes in U.S. C2H6 emissions are decoupled from reported changes in production; emissions increased 6.3 ± 7.6% (0.25 ± 0.31 Tg) between 2015 and 2020 while reported C2H6 production increased by a much larger amount (78%). Our results also suggest an apparent correlation between C2H6 emissions and C2H6 spot prices, where prices could be a proxy for pressure on the infrastructure across the supply chain. Overall, these results provide insight into how U.S. C2H6 emissions are changing over time.

Sustained Reductions of Bay Area CO2 Emissions 2018-2022

Cities represent a significant and growing portion of global carbon dioxide (CO2) emissions. Quantifying urban emissions and trends over time is needed to evaluate the efficacy of policy targeting emission reductions as well as to understand more fundamental questions about the urban biosphere. A number of approaches have been proposed to measure, report, and verify (MRV) changes in urban CO2 emissions. Here we show that a modest capital cost, spatially dense network of sensors, the Berkeley Environmental Air Quality and CO2 Network (BEACO2N), in combination with Bayesian inversions, result in a synthesis of measured CO2 concentrations and meteorology to yield an improved estimate of CO2 emissions and provide a cost-effective and accurate assessment of CO2 emissions trends over time. We describe nearly 5 years of continuous CO2 observations (2018-2022) in a midsized urban region (the San Francisco Bay Area). These observed concentrations constrain a Bayesian inversion that indicates the interannual trend in urban CO2 emissions in the region has been a modest decrease at a rate of 1.8 ± 0.3%/year. We interpret this decrease as primarily due to passenger vehicle electrification, reducing on-road emissions at a rate of 2.6 ± 0.7%/year.

Methane Emissions Show Recent Decline but Strong Seasonality in Two US Northeastern Cities

Urban methane emissions estimated using atmospheric observations have been found to exceed estimates derived by using traditional inventory methods in several northeastern US cities. In this work, we leveraged a nearly five-year record of observations from a dense tower network coupled with a newly developed high-resolution emissions map to quantify methane emission rates in Washington, DC, and Baltimore, Maryland. Annual emissions averaged over 2018-2021 were 80.1 [95% CI: 61.2, 98.9] Gg in the Washington, DC urban area and 47.4 [95% CI: 35.9, 58.5] Gg in the Baltimore urban area, with a decreasing trend of approximately 4-5% per year in both cities. We also find wintertime emissions 44% higher than summertime emissions, correlating with natural gas consumption. We further attribute a large fraction of total methane emissions to the natural gas sector using a least-squares regression on our spatially resolved estimates, supporting previous findings that natural gas systems emit the plurality of methane in both cities. This study contributes to the relatively sparse existing knowledge base of urban methane emissions sources and variability, adding to our understanding of how these emissions change in time and providing evidence to support efforts to mitigate natural gas emissions.

Practical Guide to Measuring Wetland Carbon Pools and Fluxes

Wetlands cover a small portion of the world, but have disproportionate influence on global carbon (C) sequestration, carbon dioxide and methane emissions, and aquatic C fluxes. However, the underlying biogeochemical processes that affect wetland C pools and fluxes are complex and dynamic, making measurements of wetland C challenging. Over decades of research, many observational, experimental, and analytical approaches have been developed to understand and quantify pools and fluxes of wetland C. Sampling approaches range in their representation of wetland C from short to long timeframes and local to landscape spatial scales. This review summarizes common and cutting-edge methodological approaches for quantifying wetland C pools and fluxes. We first define each of the major C pools and fluxes and provide rationale for their importance to wetland C dynamics. For each approach, we clarify what component of wetland C is measured and its spatial and temporal representativeness and constraints. We describe practical considerations for each approach, such as where and when an approach is typically used, who can conduct the measurements (expertise, training requirements), and how approaches are conducted, including considerations on equipment complexity and costs. Finally, we review key covariates and ancillary measurements that enhance the interpretation of findings and facilitate model development. The protocols that we describe to measure soil, water, vegetation, and gases are also relevant for related disciplines such as ecology. Improved quality and consistency of data collection and reporting across studies will help reduce global uncertainties and develop management strategies to use wetlands as nature-based climate solutions.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13157-023-01722-2.

Formation of an unprecedented yellow snow episode in Xinjiang on December 1, 2018

On 1 December 2018, a heavy yellow snow fell in Urumqi (87°37'E, 43°47'N) - the largest city of northwest China's Xinjiang province, which was the first case that the yellow snow has been observed in winter. The air parcel trajectories obtained from Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model and the dust surface mass concentration from Modern-Era Retrospective Analysis for Research and Applications Version 2 (MERRA-2) were adopted to identify the potential sources and transport paths of pollutants responsible for this yellow snow episode. The meteorological situation and the European Center for Medium-Range Weather Forecasts (ECMWF) forecast products have been utilized to analyze the supportive meteorological conditions. The results showed that the heavy snow in Urumqi was contaminated by the yellow dust originated in Karamay of Xinjiang province. The strong surface winds in Karamay lifted large amounts of dust into the atmosphere. Then the airborne dusts were transported to Urumqi rapidly by strong low-level winds, where precipitation in connection with the upper trough and the cold front lead to the yellow snow episode. This study can provide important scientific significance for predicting this kind of event (yellow snow).

A newly developed Lagrangian chemical transport scheme: Part 1. Simulation of a boreal forest fire plume

Forest fire research over the last several decades has improved the understanding of fire emissions and impacts. Nevertheless, the evolution of forest fire plumes remains poorly quantified and understood. Here, a Lagrangian chemical transport model, the Forward Atmospheric Stochastic Transport model coupled with the Master Chemical Mechanism (FAST-MCM), has been developed to simulate the transport and chemical transformations of plumes from a boreal forest fire over several hours since their emission. The model results for NOx (NO and NO₂), O₃, HONO, HNO₃, pNO₃ and 70 VOC species are compared with airborne in-situ measurements within plume centers and their surrounding portions during the transport. Comparisons between simulation results and measurements show that the FAST-MCM model can properly reproduce the physical and chemical evolution of forest fire plumes. The results indicate that the model can be an important tool used to aid the understanding of the downwind impacts of forest fire plumes.

The effect of military conflict zone in the Middle East on atmospheric persistent organic pollutant contamination in its north

This study aimed to investigate long-range atmospheric transport of selected POPs released due to the effects of military conflicts in regions to the south of Turkey's borders. Ten locations were selected to deploy passive air samplers at varying distances to the border on a southeast-west transect of the country, proximity-grouped as close, middle, and far. Sampling campaign included winter and transition months when desert dust transport events occur. Hypothesis of the study was that a decreasing trend would be observed with increasing distance to the border. Group comparisons based on statistical testing showed that PBDE-183, Σ₄₅PCB, and dieldrin in winter; PBDE-28, PBDE-99, PBDE-154, p,p'-DDE, Σ₁₄PBDE, and Σ₂₅OCP in the transition period; and PBDE-28, PBDE-85, PBDE-99, PBDE-154, PBDE-190, PCB-52, Σ₄₅PCB, p,p'-DDE, and Σ₂₅OCP over the whole campaign had a decreasing trend on the transect. An analysis of concentration ratio to the background showed that long-range atmospheric transport impacted the study sites, especially those of close group in comparison to the local sources. Back-trajectory analyses indicated that there was transport from the conflict areas to sites in the close-proximity group, while farther sampling locations mostly received air masses from Europe, Russia, and former Soviet Union countries, followed by North Africa, rather than the military conflict areas. In consequence, decrease in concentrations with distance and its relation to molecular weight through proportions, diagnostic ratios, analysis of concentration ratio to the background, and back-trajectory analyses support the effect of transport from the military-conflict area to its north.

Continuous atmospheric in-situ measurements of the CH4/CO ratio at the Mt. Cimone station (Italy, 2165 m a.s.l.) and their possible use for estimating regional CH4 emissions

Methane (CH₄) is an important climate forcer, contributing about 17% of the total radiative forcing by long living greenhouse gases. The Po basin is one of the most polluted and densely populated areas in Europe representing an important source region for CH₄. The aim of this work was to test an inter-species correlation approach to derive estimates of anthropogenic CH₄ emissions for the period 2015-2019 from the Po basin by combining CO bottom-up inventory data and continuous CH₄ and CO observations from a mountain site in the northern Italy. The tested methodology suggested lower emissions in respect to EDGAR (-17%) and the Italian National Inventory (-40%) for the Po basin. However, despite the two bottom-up inventories, the emissions derived from the atmospheric observations reported an increasing tendency from 2015 to 2019 for the CH₄ emissions. A sensitivity study revealed that using different subsets of the atmospheric observations implied a difference of 26% in the CH₄ emission estimates. The highest agreement with two bottom-up CH₄ inventories (EDGAR and the Italian national inventory) were obtained when atmospheric data were strictly selected for periods representative of air mass transport from the Po basin. Our study identified various challenges when using this methodology as a benchmark to verify bottom-up CH₄ inventories. Issues could be attributed to the annual aggregation of the proxies used to derive the emission amounts, to the CO bottom-up inventory used as input information and to the relatively high sensitivity of the results to the different subsets of the atmospheric observations. However, the use of different bottom-up inventories as input data for CO emissions can potentially provide information that should be carefully considered for the purpose of integrating CH₄ bottom-up inventories.

Oil trajectory analysis for oil spill surveillance by SAR in the Mediterranean Sea

Oil trajectory analysis (OTA) provides statistics of direction and distance of provenience of oil spills reaching specific coastal sites. Applied to marine protected areas (MPA), this information could be used to introduce priority criteria in satellite oil spill surveillance. OTA in the Mediterranean Sea was based on 10-days oil trajectories tracked backward-in-time for five years (2015-2019) and aggregated on monthly basis. On average, travel time increases from 12 h at 5 km from the coast to 1.5 days at 10 km and 2 days at 15 km. The beaching probability decreases from 25 % at 5 km to 8 % at 10 km and 5 % at 15 km. Locally, the oil transport is influenced by persistent winds and/or energetic current systems in the area. Using an attention threshold of 5 % of beaching probability around MPA, several offshore areas of the Mediterranean Sea deserving high monitoring priority in summer and winter have been identified.

"Urban Respiration" Revealed by Atmospheric O2 Measurements in an Industrial Metropolis

Urban regions, which "inhale" O₂ from the air and "exhale" CO₂ and atmospheric pollutants, including harmful gases and fine particles, are the largest sinks of atmospheric O₂, yet long-term O₂ measurements in urban regions are currently lacking. In this study, we report continuous measurements of atmospheric O₂ in downtown Lanzhou, an industrial metropolis in northwestern China. We found declines in atmospheric O₂ associated with deteriorated air quality and robust anticorrelations between O₂ and gaseous oxides. By combining O₂ and pollutants measurements with a Lagrangian atmospheric transport model, we quantitatively break down "urban respiration" (ΔO_2URB) into human respiration (ΔO_2RES) and fossil fuel combustion (ΔO_2FF). We found increased ΔO_2FF contribution (from 66.92% to 72.50%) and decreased ΔO_2RES contribution (from 33.08 to 27.50%) as O₂ declines and pollutants accumulate. Further attribution of ΔO_2FF reveals intracity transport of atmospheric pollutants from industrial sectors and suggests transportation sectors as the major O₂ sink in downtown Lanzhou. The varying relationships between O₂ and pollutants under different conditions unfold the dynamics of urban respiration and provide insights into the O₂ and energy consumption, pollutant emission, and intracity atmospheric transport processes.

Midlatitude Ozone Depletion and Air Quality Impacts from Industrial Halogen Emissions in the Great Salt Lake Basin

We report aircraft observations of extreme levels of HCl and the dihalogens Cl₂, Br₂, and BrCl in an industrial plume near the Great Salt Lake, Utah. Complete depletion of O₃ was observed concurrently with halogen enhancements as a direct result of photochemically produced halogen radicals. Observed fluxes for Cl₂, HCl, and NO_x agreed with facility-reported emissions inventories. Bromine emissions are not required to be reported in the inventory, but are estimated as 173 Mg year^-1 Br₂ and 949 Mg year^-1 BrCl, representing a major uncounted oxidant source. A zero-dimensional photochemical box model reproduced the observed O₃ depletions and demonstrated that bromine radical cycling was principally responsible for the rapid O₃ depletion. Inclusion of observed halogen emissions in both the box model and a 3D chemical model showed significant increases in oxidants and particulate matter (PM_2.5) in the populated regions of the Great Salt Lake Basin, where winter PM_2.5 is among the most severe air quality issues in the U.S. The model shows regional PM_2.5 increases of 10%-25% attributable to this single industrial halogen source, demonstrating the impact of underreported industrial bromine emissions on oxidation sources and air quality within a major urban area of the western U.S.

Unusual early peaks of airborne ragweed (Ambrosia L.) pollen in the Pannonian Biogeographical Region

Early peaks of airborne ragweed (Ambrosia L.) pollen concentrations were observed at several monitoring stations in Hungary in June 2017 and 2018, one month before the usual start of the pollen season at the end of July. Backward trajectories were calculated to simulate potential sources of pollen collected at different locations in the Pannonian Biogeographical Region. In a collaboration between aerobiological and phenological networks, a nationwide campaign was conducted to collect field data of ragweed blooming. During field surveys, ragweed plants having extremely early blooming were found most abundantly in a rural site near Vaja (North-East Hungary) and other locations in Hungary. Field observations matched with source areas identified by trajectory analyses; i.e., early-flowering ragweed plants were found at some of these locations. Although similar peaks of airborne pollen concentrations were not detected in other years (e.g., 2016, 2019-2021), alarming results suggest the possibility of expanding seasons of ragweed allergy.

The Nord Stream pipeline gas leaks released approximately 220,000 tonnes of methane into the atmosphere

Sudden mega natural gas leaks of two Nord Stream pipelines in the Baltic Sea (Denmark) occurred from late September to early October 2022, releasing large amounts of methane into the atmosphere. We inferred the methane emissions of this event based on surface in situ observations using two inversion methods and two meteorological reanalysis datasets, supplemented with satellite-based observations. We conclude that approximately 220 ± 30 Gg of methane was released from September 26 to October 1, 2022.

Characterization of Regional Combustion Efficiency using ΔXCO: ΔXCO2 Observed by a Portable Fourier-Transform Spectrometer at an Urban Site in Beijing

Measurements of column-averaged dry-air mole fractions of carbon dioxide and carbon monoxide, CO₂ (XCO₂) and CO (XCO), were performed throughout 2019 at an urban site in Beijing using a compact Fourier Transform Spectrometer (FTS) EM27/SUN. This data set is used to assess the characteristics of combustion-related CO₂ emissions of urban Beijing by analyzing the correlated daily anomalies of XCO and XCO₂ (e.g., ΔXCO and ΔXCO₂). The EM27/SUN measurements were calibrated to a 125HR-FTS at the Xianghe station by an extra EM27/SUN instrument transferred between two sites. The ratio of ΔXCO over ΔXCO₂ (ΔXCO:ΔXCO₂) is used to estimate the combustion efficiency in the Beijing region. A high correlation coefficient (0.86) between ΔXCO and ΔXCO₂ is observed. The CO:CO₂ emission ratio estimated from inventories is higher than the observed ΔXCO:ΔXCO₂ (10.46 ± 0.11 ppb ppm^-1) by 42.54%-101.15%, indicating an underestimation in combustion efficiency in the inventories. Daily ΔXCO:ΔXCO₂ are influenced by transportation governed by weather conditions, except for days in summer when the correlation is low due to the terrestrial biotic activity. By convolving the column footprint [ppm (µmol m^-2 s^-1)^-1] generated by the Weather Research and Forecasting-X-Stochastic Time-Inverted Lagrangian Transport models (WRF-X-STILT) with two fossil-fuel emission inventories (the Multi-resolution Emission Inventory for China (MEIC) and the Peking University (PKU) inventory), the observed enhancements of CO₂ and CO were used to evaluate the regional emissions. The CO₂ emissions appear to be underestimated by 11% and 49% for the MEIC and PKU inventories, respectively, while CO emissions were overestimated by MEIC (30%) and PKU (35%) in the Beijing area.

Novel quantification of regional fossil fuel CO2 reductions during COVID-19 lockdowns using atmospheric oxygen measurements

It is not currently possible to quantify regional-scale fossil fuel carbon dioxide (ffCO₂) emissions with high accuracy in near real time. Existing atmospheric methods for separating ffCO₂ from large natural carbon dioxide variations are constrained by sampling limitations, so that estimates of regional changes in ffCO₂ emissions, such as those occurring in response to coronavirus disease 2019 (COVID-19) lockdowns, rely on indirect activity data. We present a method for quantifying regional signals of ffCO₂ based on continuous atmospheric measurements of oxygen and carbon dioxide combined into the tracer "atmospheric potential oxygen" (APO). We detect and quantify ffCO₂ reductions during 2020-2021 caused by the two U.K. COVID-19 lockdowns individually using APO data from Weybourne Atmospheric Observatory in the United Kingdom and a machine learning algorithm. Our APO-based assessment has near-real-time potential and provides high-frequency information that is in good agreement with the spread of ffCO₂ emissions reductions from three independent lower-frequency U.K. estimates.

Testing HYSPLIT Plume Dispersion Model Performance Using Regional Hydrocarbon Monitoring Data during a Gas Well Blowout

A gas well blowout in south central Texas in November 2019 that lasted for 20 days provided a unique opportunity to test the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model’s plume dispersion against hydrocarbon air monitoring data at two nearby state monitoring stations. We estimated daily blowout hydrocarbon emission rates from satellite measurement-based results on methane emissions in conjunction with previously reported composition data of the local hydrocarbon resource. Using highly elevated hydrocarbon mixing ratios observed during several days at the two downwind monitoring stations, we calculated excess abundances above expected local background mixing ratios. Subsequent comparisons to HYSPLIT plume dispersion model outputs, generated using High-Resolution Rapid Refresh (HRRR) or North American Mesoscale (NAM) forecast meteorological input data, showed that the model generally reproduces both the timing and magnitude of the plume in various meteorological conditions. Absolute hydrocarbon mixing ratios could typically be reproduced within a factor of two. However, when lower emission rate estimates provided by the company in charge of the well were used, downwind hydrocarbon observations could not be reproduced. Overall, our results suggest that HYSPLIT, in combination with high-resolution meteorological input data, is a useful tool to accurately forecast chemical plume dispersion and potential human exposure in disaster situations.

Carbon Monoxide Emissions from the Washington, DC, and Baltimore Metropolitan Area: Recent Trend and COVID-19 Anomaly

We analyze airborne measurements of atmospheric CO concentration from 70 flights conducted over six years (2015-2020) using an inverse model to quantify the CO emissions from the Washington, DC, and Baltimore metropolitan areas. We found that CO emissions have been declining in the area at a rate of ≈-4.5 % a^-1 since 2015 or ≈-3.1 % a^-1 since 2016. In addition, we found that CO emissions show a "Sunday" effect, with emissions being lower, on average, than for the rest of the week and that the seasonal cycle is no larger than 16 %. Our results also show that the trend derived from the NEI agrees well with the observed trend, but that NEI daytime-adjusted emissions are ≈50 % larger than our estimated emissions. In 2020, measurements collected during the shutdown in activity related to the COVID-19 pandemic indicate a significant drop in CO emissions of 16 % relative to the expected emissions trend from the previous years, or 23 % relative to the mean of 2016 to February 2020. Our results also indicate a larger reduction in April than in May. Last, we show that this reduction in CO emissions was driven mainly by a reduction in traffic.

The CO2 record at the Amazon Tall Tower Observatory: A new opportunity to study processes on seasonal and inter-annual scales

High-quality atmospheric CO₂  measurements are sparse in Amazonia, but can provide critical insights into the spatial and temporal variability of sources and sinks of CO₂ . In this study, we present the first 6 years (2014-2019) of continuous, high-precision measurements of atmospheric CO₂ at the Amazon Tall Tower Observatory (ATTO, 2.1°S, 58.9°W). After subtracting the simulated background concentrations from our observational record, we define a CO₂ regional signal ( ΔCO2obs ) that has a marked seasonal cycle with an amplitude of about 4 ppm. At both seasonal and inter-annual scales, we find differences in phase between ΔCO2obs and the local eddy covariance net ecosystem exchange (EC-NEE), which is interpreted as an indicator of a decoupling between local and non-local drivers of ΔCO2obs . In addition, we present how the 2015-2016 El Niño-induced drought was captured by our atmospheric record as a positive 2σ anomaly in both the wet and dry season of 2016. Furthermore, we analyzed the observed seasonal cycle and inter-annual variability of ΔCO2obs together with net ecosystem exchange (NEE) using a suite of modeled flux products representing biospheric and aquatic CO₂ exchange. We use both non-optimized and optimized (i.e., resulting from atmospheric inverse modeling) NEE fluxes as input in an atmospheric transport model (STILT). The observed shape and amplitude of the seasonal cycle was captured neither by the simulations using the optimized fluxes nor by those using the diagnostic Vegetation and Photosynthesis Respiration Model (VPRM). We show that including the contribution of CO₂ from river evasion improves the simulated shape (not the magnitude) of the seasonal cycle when using a data-driven non-optimized NEE product (FLUXCOM). The simulated contribution from river evasion was found to be 25% of the seasonal cycle amplitude. Our study demonstrates the importance of the ATTO record to better understand the Amazon carbon cycle at various spatial and temporal scales.

Mars Methane Sources in Northwestern Gale Crater Inferred From Back Trajectory Modeling

During its first seven years of operation, the Sample Analysis at Mars Tunable Laser Spectrometer (TLS) on board the Curiosity rover has detected seven methane spikes above a low background abundance in Gale crater. The methane spikes are likely sourced by surface emission within or around Gale crater. Here, we use inverse Lagrangian modeling techniques to identify upstream emission regions on the Martian surface for these methane spikes at an unprecedented spatial resolution. Inside Gale crater, the northwestern crater floor casts the strongest influence on the detections. Outside Gale crater, the upstream regions common to all the methane spikes extend toward the north. The contrasting results from two consecutive TLS methane measurements performed on the same sol point to an active emission site to the west or the southwest of the Curiosity rover on the northwestern crater floor. The observed spike magnitude and frequency also favor emission sites on the northwestern crater floor, unless there are fast methane removal mechanisms at work, or either the methane spikes of TLS or the non-detections of ExoMars Trace Gas Orbiter cannot be trusted.

Insight into ozone profile climatology over northeast China from aircraft measurement and numerical simulation

Tropospheric ozone is a major pollutant that can harm human health, animals and plants. With a rapid development in Northeast China, ozone pollution has become an increasingly serious environmental challenge. To study the ozone distribution and the potential sources of ozone precursors in Northeast China, we analyzed vertical ozone profiles from the In-service Aircraft for a Global Observing System (IAGOS) in 2012-2014 and provided the climatological vertical structure of tropospheric ozone over Shenyang. The tropospheric ozone generally presents high in hot months, mainly due to the combined effects of the strong solar radiation and high volatile organic compounds emission in summer. While in cold months, the ozone is low because of weak solar radiation and high nitrogen oxides emission. Besides, a low-ozone center exists within lower troposphere in August, which is mainly caused by the East Asian summer monsoon prevailing in summer. To analyze the sources of ozone, typical ozone pollution episodes were studied and the results revealed the different pathways for the enhancement of ozone pollution in Shenyang: regional transport of anthropogenic emissions from North China Plain (NCP), long-range transport from Siberian biomass burning and local photochemical production. Modeling results show that the largest contribution to the surface ozone in Northeast China is local anthropogenic emissions (exceed 90%); the regional transport of NCP anthropogenic emissions contribute more to the pollutants around 2 km, and account for more than 50% pollutants during highly ozone polluted days; through long-range transport, Siberian biomass burning in the spring also have a nonnegligible effect on the near-ground ozone in Northeast China. Overall, this study provides tropospheric ozone climatology and its source attribution in Northeast China, and highlight the great importance of regional transport of anthropogenic and biomass burning emissions in ozone pollution.

Accounting for Transport Error in Inversions: An Urban Synthetic Data Experiment

We present and discuss the use of a high-dimensional computational method for atmospheric inversions that incorporates the space-time structure of transport and dispersion errors. In urban environments, transport and dispersion errors are largely the result of our inability to capture the true underlying transport of greenhouse gas (GHG) emissions to observational sites. Motivated by the impact of transport model error on estimates of fluxes of GHGs using in situ tower-based mole-fraction observations, we specifically address the need to characterize transport error structures in high-resolution large-scale inversion models. We do this using parametric covariance functions combined with shrinkage-based regularization methods within an Ensemble Transform Kalman Filter inversion setup. We devise a synthetic data experiment to compare the impact of transport and dispersion error component of the model-data mismatch covariance choices on flux retrievals and study the robustness of the method with respect to fewer observational constraints. We demonstrate the analysis in the context of inferring CO2 fluxes starting with a hypothesized prior in the Washington D.C. /Baltimore area constrained by a synthetic set of tower-based CO2 measurements within an observing system simulation experiment framework. This study demonstrates the ability of these simple covariance structures to substantially improve the estimation of fluxes over standard covariance models in flux estimation from urban regions.

The Impact of COVID-19 on CO2 Emissions in the Los Angeles and Washington DC/Baltimore Metropolitan Areas

Responses to COVID-19 have resulted in unintended reductions of city-scale carbon dioxide (CO2) emissions. Here, we detect and estimate decreases in CO2 emissions in Los Angeles and Washington DC/Baltimore during March and April 2020. We present three lines of evidence using methods that have increasing model dependency, including an inverse model to estimate relative emissions changes in 2020 compared to 2018 and 2019. The March decrease (25%) in Washington DC/Baltimore is largely supported by a drop in natural gas consumption associated with a warm spring whereas the decrease in April (33%) correlates with changes in gasoline fuel sales. In contrast, only a fraction of the March (17%) and April (34%) reduction in Los Angeles is explained by traffic declines. Methods and measurements used herein highlight the advantages of atmospheric CO2 observations for providing timely insights into rapidly changing emissions patterns that can empower cities to course-correct CO2 reduction activities efficiently.

Wintertime Nitrous Oxide Emissions in the San Joaquin Valley of California Estimated from Aircraft Observations

Nitrous oxide (N₂O) is a long-lived greenhouse gas that also destroys stratospheric ozone. N₂O emissions are uncertain and characterized by high spatiotemporal variability, making individual observations difficult to upscale, especially in mixed land use source regions like the San Joaquin Valley (SJV) of California. Here, we calculate spatially integrated N₂O emission rates using nocturnal and convective boundary-layer budgeting methods. We utilize vertical profile measurements from the NASA DISCOVER-AQ (Deriving Information on Surface Conditions from COlumn and VERtically Resolved Observations Relevant to Air Quality) campaign, which took place January-February, 2013. For empirical constraints on N₂O source identity, we analyze N₂O enhancement ratios with methane, ammonia, carbon dioxide, and carbon monoxide separately in the nocturnal boundary layer, nocturnal residual layer, and convective boundary layer. We find that an established inventory (EDGAR v4.3.2) underestimates N₂O emissions by at least a factor of 2.5, that wintertime emissions from animal agriculture are important to annual totals, and that there is evidence for higher N₂O emissions during the daytime than at night.

An Interpolation Method to Reduce the Computational Time in the Stochastic Lagrangian Particle Dispersion Modeling of Spatially Dense XCO2 Retrievals

A growing constellation of satellites is providing near-global coverage of column-averaged CO2 observations. Launched in 2019, NASA's OCO-3 instrument is set to provide XCO2 observations at a high spatial and temporal resolution for regional domains (100 × 100 km). The atmospheric column version of the Stochastic Time-Inverted Lagrangian Transport (X-STILT) model is an established method of determining the influence of upwind sources on column measurements of the atmosphere, providing a means of analysis for current OCO-3 observations and future space-based column-observing missions. However, OCO-3 is expected to provide hundreds of soundings per targeted observation, straining this already computationally intensive technique. This work proposes a novel scheme to be used with the X-STILT model to generate upwind influence footprints with less computational expense. The method uses X-STILT generated influence footprints from a key subset of OCO-3 soundings. A nonlinear weighted averaging is applied to these footprints to construct additional footprints for the remaining soundings. The effects of subset selection, meteorological data, and topography are investigated for two test sites: Los Angeles, California, and Salt Lake City, Utah. The computational time required to model the source sensitivities for OCO-3 interpretation was reduced by 62% and 78% with errors smaller than other previously acknowledged uncertainties in the modeling system (OCO-3 retrieval error, atmospheric transport error, prior emissions error, etc.). Limitations and future applications for future CO2 missions are also discussed.

Transboundary transport of ozone pollution to a US border region: A case study of Yuma

High concentrations of ground-level ozone affect human health, plants, and animals. Reducing ozone pollution in rural regions, where local emissions are already low, poses challenge. We use meteorological back-trajectories, air quality model sensitivity analysis, and satellite remote sensing data to investigate the ozone sources in Yuma, Arizona and find strong international influences from Northern Mexico on 12 out of 16 ozone exceedance days. We find that such exceedances could not be mitigated by reducing emissions in Arizona; complete removal of state emissions would reduce the maximum daily 8-h average (MDA8) ozone in Yuma by only 0.7% on exceeding days. In contrast, emissions in Mexico are estimated to contribute to 11% of the ozone during these exceedances, and their reduction would reduce MDA8 ozone in Yuma to below the standard. Using satellite-based remote sensing measurements, we find that emissions of nitrogen oxides (NOx, a key photochemical precursor of ozone) increase slightly in Mexico from 2005 to 2016, opposite to decreases shown in the bottom-up inventory. In comparison, a decrease of NOx emissions in the US and meteorological factors lead to an overall of summer mean and annual MDA8 ozone in Yuma (by ∼1-4% and ∼3%, respectively). Analysis of meteorological back-trajectories also shows similar transboundary transport of ozone at the US-Mexico border in California and New Mexico, where strong influences from Northern Mexico coincide with 11 out of 17 and 6 out of 8 ozone exceedances. 2020 is the final year of the U.S.-Mexico Border 2020 Program, which aimed to reduce pollution at border regions of the US and Mexico. Our results indicate the importance of sustaining a substantial cooperative program to improve air quality at the border area.

Background conditions for an urban greenhouse gas network in the Washington, D.C. and Baltimore metropolitan region

As city governments take steps towards establishing emissions reduction targets, the atmospheric research community is increasingly able to assist in tracking emissions reductions. Researchers have established systems for observing atmospheric greenhouse gases in urban areas with the aim of attributing greenhouse gas concentration enhancements (and thus, emissions) to the region in question. However, to attribute enhancements to a particular region, one must isolate the component of the observed concentration attributable to fluxes inside the region by removing the background, which is the component due to fluxes outside. In this study, we demonstrate methods to construct several versions of a background for our carbon dioxide and methane observing network in the Washington, DC and Baltimore, MD metropolitan region. Some of these versions rely on transport and flux models, while others are based on observations upwind of the domain. First, we evaluate the backgrounds in a synthetic data framework, then we evaluate against real observations from our urban network. We find that backgrounds based on upwind observations capture the variability better than model-based backgrounds, although care must be taken to avoid bias from biospheric carbon dioxide fluxes near background stations in summer. Model-based backgrounds also perform well when upwind fluxes can be modeled accurately. Our study evaluates different background methods and provides guidance determining background methodology that can impact the design of urban monitoring networks.

Constraining Urban CO2 Emissions Using Mobile Observations from a Light Rail Public Transit Platform

Urban environments are characterized by pronounced spatiotemporal heterogeneity, which can present sampling challenges when utilizing conventional greenhouse gas (GHG) measurement systems. In Salt Lake City, Utah, a GHG instrument was deployed on a light rail train car that continuously traverses the Salt Lake Valley (SLV) through a range of urban typologies. CO₂ measurements from a light rail train car were used within a Bayesian inverse modeling framework to constrain urban emissions across the SLV during the fall of 2015. The primary objectives of this study were to (1) evaluate whether ground-based mobile measurements could be used to constrain urban emissions using an inverse modeling framework and (2) quantify the information that mobile observations provided relative to conventional GHG monitoring networks. Preliminary results suggest that ingesting mobile measurements into an inverse modeling framework generated a posterior emission estimate that more closely aligned with observations, reduced posterior emission uncertainties, and extends the geographical extent of emission adjustments.

Eight-Year Estimates of Methane Emissions from Oil and Gas Operations in Western Canada Are Nearly Twice Those Reported in Inventories

The provinces of Alberta and Saskatchewan account for 70% of Canada's methane emissions from the oil and gas sector. In 2018, the Government of Canada introduced methane regulations to reduce emissions from the sector by 40-45% from the 2012 levels by 2025. Complementary to inventory accounting methods, the effectiveness of regulatory practices to reduce emissions can be assessed using atmospheric measurements and inverse models. Total anthropogenic (oil and gas, agriculture, and waste) emission rates of methane from 2010 to 2017 in Alberta and Saskatchewan were derived using hourly atmospheric methane measurements over a six-month winter period from October to March. Scaling up the winter estimate to annual indicated an anthropogenic emission rate of 3.7 ± 0.7 MtCH₄/year, about 60% greater than that reported in Canada's National Inventory Report (2.3 MtCH₄). This discrepancy is tied primarily to the oil and gas sector emissions as the reported emissions from livestock operations (0.6 MtCH₄) are well substantiated in both top-down and bottom-up estimates and waste management (0.1 MtCH₄) emissions are small. The resulting estimate of 3.0 MtCH₄ from the oil and gas sector is nearly twice that reported in Canada's National Inventory (1.6 MtCH₄).

Atmospheric inverse estimates of CO emissions from Zhengzhou, China

Carbon monoxide (CO) is an important gas that affects human health and causes air pollution. However, the estimates of CO emissions in China are still subject to large uncertainties. Based on the CO mass concentration and the coupled Weather Research and Forecast (WRF) and Stochastic Time-Inverted Lagrangian Transport (STILT) model (WRF-STILT), this study estimates the CO emissions over Zhengzhou, China. The results show that the mean CO mass concentration was 1.17 mg m^-3 from November 2017 to February 2018, with a clear diurnal variation. There were two periods of rapidly increasing CO concentration in the diurnal variation, which are 06:00-09:00 and 16:00-20:00 local time. The footprint analysis shows that the observation site is highly influenced by local emissions. The most influential regions to the site observations are northeast and northwest Zhengzhou, which are associated with the geographical barrier of the Taihang Mountains in the north and narrow Fenwei Plain in the west. The inversion result shows that the actual emissions are lower than the inventory estimates. Using the optimal scaling factors, the WRF-STILT simulations of CO concentration agree closely with the CO measurements with the linear fitting regression equation y = 0.87x + 0.15. The slopes of the linear fitting regressions between the WRF-STILT-simulated CO concentrations determined using the optimal emissions and the observations range from 0.72 to 0.89 for four months, and all the fitting results passed the significance test (P < 0.001). These results indicate that the new optimal emissions derived with the scaling factors could better represent the real emission conditions than the a priori emissions if the WRF-STILT model is assumed to be reliable.

Determination of Region of Influence Obtained by Aircraft Vertical Profiles Using the Density of Trajectories from the HYSPLIT Model

Aircraft atmospheric profiling is a valuable technique for determining greenhouse gas fluxes at regional scales (104–106 km2). Here, we describe a new, simple method for estimating the surface influence of air samples that uses backward trajectories based on the Lagrangian model Hybrid Single-Particle Lagrangian Integrated Trajectory Model (HYSPLIT). We determined “regions of influence” on a quarterly basis between 2010 and 2018 for four aircraft vertical profile sites: SAN and ALF in the eastern Amazon, and RBA and TAB or TEF in the western Amazon. We evaluated regions of influence in terms of their relative sensitivity to areas inside and outside the Amazon and their total area inside the Amazon. Regions of influence varied by quarter and less so by year. In the first and fourth quarters, the contribution of the region of influence inside the Amazon was 83–93% for all sites, while in the second and third quarters, it was 57–75%. The interquarter differences are more evident in the eastern than in the western Amazon. Our analysis indicates that atmospheric profiles from the western sites are sensitive to 42–52.2% of the Amazon. In contrast, eastern Amazon sites are sensitive to only 10.9–25.3%. These results may help to spatially resolve the response of greenhouse gas emissions to climate variability over Amazon.

Comparisons of forward-in-time and backward-in-time Lagrangian stochastic dispersion models for micro-scale atmospheric dispersion

Lagrangian stochastic dispersion models are sometimes run in backward mode to estimate air emissions from different types of sources including area sources. The forward-in-time and backward-in-time Lagrangian stochastic (fLS and bLS) dispersion models may not result in the same estimates. The two models were compared under different atmospheric conditions in micro-scale applications. They are equivalent in a steady-state and horizontally homogeneous atmosphere in many features including estimating concentration at a point, using surface receptor, and prerunning the models. Although bLS shows better computational efficiency, it has a larger uncertainty in results due to the use of surface receptors. In a non-steady-state wind field, the two models show opposite transition trends when the wind fields experience a step change. Under sinusoidal-varying winds, the two models show different shapes of the predicated concentration curves. The normalized differences of the mean concentrations mainly increase with the receptor height when the source-receptor distance is fixed. A controlled methane release experiment was conducted to investigate the behaviors of the two models driven by real wind fields. The correlation coefficient between model-predicted concentrations is 0.95. The model-predicted (forward model) and measured concentrations show similar trend with a correlation coefficient of 0.70. The bLS model estimates larger peak concentrations than that fLS model under the same emission rate. The best-fitted results of the fLS and bLS models give recovery ratios of 1.1558 and 0.9675, respectively, which are better than that using a constant 15-min averaged wind (0.7922).Implications: There are large uncertainties and difficulties in quantification of fugitive air emissions from area sources such as landfills, agriculture, and industry sections. Lagrangian stochastic dispersion model is a versatile tool for these applications with the capability of near-field description and good efficiency. Backward models are usually used to estimate emission rates from area sources due to high computing efficiencies. But they may not result in the same estimate as the forward models due to factors involving model realization and input parameters. It is necessary to investigate the discrepancies to select the best model with minimal uncertainty in the results.

Greenhouse gas observations from the Northeast Corridor tower network

We present the organization, structure, instrumentation, and measurements of the Northeast Corridor greenhouse gas observation network. This network of tower-based in situ carbon dioxide and methane observation stations was established in 2015 with the goal of quantifying emissions of these gases in urban areas in the northeastern United States. A specific focus of the network is the cities of Baltimore, MD, and Washington, DC, USA, with a high density of observation stations in these two urban areas. Additional observation stations are scattered throughout the northeastern US, established to complement other existing urban and regional networks and to investigate emissions throughout this complex region with a high population density and multiple metropolitan areas. Data described in this paper are archived at the National Institute of Standards and Technology and can be found at https://doi.org/10.18434/M32126 (Karion et al., 2019).

Assessment of Planetary Boundary Layer parametrizations and urban heat island comparison: Impacts and implications for tracer transport

Accurate simulation of planetary boundary layer height (PBLH) is key to greenhouse gas emission estimation, air quality prediction and weather forecasting. This manuscript describes an extensive performance assessment of several Weather Research and Forecasting (WRF) model configurations where novel observations from ceilometers, surface stations and a flux tower were used to study their ability to reproduce planetary boundary layer heights (PBLH) and the impact that the urban heat island (UHI) has on the modeled PBLHs in the greater Washington, D.C. area. In addition, CO₂ measurements at two urban towers were compared to tracer transport simulations. The ensemble of models used 4 PBL parameterizations, 2 sources of initial and boundary conditions and 1 configuration including the building energy parameterization (BEP) urban canopy model. Results have shown low biases over the whole domain and period for wind speed, wind direction and temperature with no drastic differences between meteorological drivers. We find that PBLH errors are mostly positively correlated with sensible heat flux errors, and that modeled positive UHI intensities are associated with deeper modeled PBLs over the urban areas. In addition, we find that modeled PBLHs are typically biased low during nighttime for most of the configurations with the exception of those using the MYNN parametrization and that these biases directly translate to tracer biases. Overall, the configurations using MYNN scheme performed the best, reproducing the PBLH and CO₂ molar fractions reasonably well during all hours, thus opening the door to future nighttime inverse modeling.

Seasonally Resolved Excess Urban Methane Emissions from the Baltimore/Washington, DC Metropolitan Region

Urban areas are increasingly recognized as an important source of methane (CH₄), but we have limited seasonally resolved observations of these regions. In this study, we quantify seasonal and annual urban CH₄ emissions over the Baltimore, Maryland, and Washington, DC metropolitan regions. We use CH₄ atmospheric observations from four tall tower stations and a Lagrangian particle dispersion model to simulate CH₄ concentrations at these stations. We directly compare these simulations with observations and use a geostatistical inversion method to determine optimal emissions to match our observations. We use observations spanning four seasons and employ an ensemble approach considering multiple meteorological representations, emission inventories, and upwind CH₄ values. Forward simulations in winter, spring, and fall underestimate observed atmospheric CH₄ while in summer, simulations overestimate observations because of excess modeled wetland emissions. With ensemble geostatistical inversions, the optimized annual emissions in DC/Baltimore are 39 ± 9 Gg/month (1 δ), 2.0 ± 0.4 times higher than the ensemble mean of bottom-up emission inventories. We find a modest seasonal variability of urban CH₄ emissions not captured in current inventories, with optimized summer emissions ∼41% lower than winter, broadly consistent with expectations if emissions are dominated by fugitive natural gas sources that correlate with natural gas usage.

Using Lidar Technology To Assess Urban Air Pollution and Improve Estimates of Greenhouse Gas Emissions in Boston

Simulation of the planetary boundary layer (PBL) is key for forecasting air quality and estimating greenhouse gas (GHG) emissions in cities. Here we conducted the first long-term and continuous study of PBL heights (PBLHs) in Boston, MA, using a compact lidar instrument. We developed an image recognition algorithm to estimate PBLHs from the lidar measurements and evaluated simulations of the PBL from seven numerical weather prediction (NWP) model versions, which showed different systematic errors and variability in simulating the PBLHs (discrepancies from -2.5 to 4.0 km). The NWP model with the best overall agreement for the fully developed PBL had R² = 0.72 and a bias of only 0.128 km. However, this model predicted a notable number of anomalously high carbon dioxide concentrations at ground stations, because it occasionally significantly underestimated the PBLH. We also developed a novel method that combines lidar data with footprints from a Lagrangian particle dispersion model to identify long-range transport of air pollution in the nocturnal residual layer. Our framework was powerful in evaluating the performance of models used to estimate air pollution and GHG emissions in cities, which is critical to track progress on emission reduction targets and guide effective policies.

Fine-resolution mapping of particulate matter concentration in urban areas and population exposure analysis via dispersion modeling: a study in Daejeon, South Korea

To deliver accurate particulate matter information to citizens, a detailed particulate matter dispersion model including factors such as land use and meteorological information was developed and used to create particulate matter concentration distribution maps for Daejeon Metropolitan City (South Korea). The results showed differences from existing particulate matter concentration distribution maps created using established methods. For PM2.5, approximately 3600 concentration maps were constructed. Taking a map as an example, according to existing methods, the PM2.5 concentration was "good" in 56% and "moderate" in 44% of areas. However, according to our modeling, the PM2.5 concentration was good in 31%, moderate in 26%, "unhealthy" in 28%, and "very unhealthy" in 15% of areas. Furthermore, the existing methods indicated that no portion of the population was exposed to poor particulate matter concentrations, while the proposed model found that over 170,000 people were exposed to such concentrations. These results will contribute to sustainable urban and environmental planning.

Atmospheric observation-based estimation of fossil fuel CO2 emissions from regions of central and southern California

Combustion of fossil fuel is the dominant source of greenhouse gas emissions to the atmosphere in California. Here, we describe radiocarbon (¹⁴CO₂) measurements and atmospheric inverse modeling to estimate fossil fuel CO₂ (ffCO₂) emissions for 2009-2012 from a site in central California, and for June 2013-May 2014 from two sites in southern California. A priori predicted ffCO₂ mixing ratios are computed based on regional atmospheric transport model (WRF-STILT) footprints and an hourly ffCO₂ prior emission map (Vulcan 2.2). Regional inversions using observations from the central California site suggest that emissions from the San Francisco Bay Area (SFBA) are higher in winter and lower in summer. Taking all years together, the average of a total of fifteen 3-month inversions from 2009 to 2012 suggests ffCO₂ emissions from SFBA were within 6 ± 35% of the a priori estimate for that region, where posterior emission uncertainties are reported as 95% confidence intervals. Results for four 3-month inversions using measurements in Los Angeles South Coast Air Basin (SoCAB) during June 2013-May 2014 suggest that emissions in SoCAB are within 13 ± 28% of the a priori estimate for that region, with marginal detection of any seasonality. While emissions from the SFBA and SoCAB urban regions (containing ~50% of prior emissions from California) are constrained by the observations, emissions from the remaining regions are less constrained, suggesting that additional observations will be valuable to more accurately estimate total ffCO₂ emissions from California as a whole.

Intercomparison of atmospheric trace gas dispersion models: Barnett Shale case study

Greenhouse gas emissions mitigation requires understanding the dominant processes controlling fluxes of these trace gases at increasingly finer spatial and temporal scales. Trace gas fluxes can be estimated using a variety of approaches that translate observed atmospheric species mole fractions into fluxes or emission rates, often identifying the spatial and temporal characteristics of the emission sources as well. Meteorological models are commonly combined with tracer dispersion models to estimate fluxes using an inverse approach that optimizes emissions to best fit the trace gas mole fraction observations. One way to evaluate the accuracy of atmospheric flux estimation methods is to compare results from independent methods, including approaches in which different meteorological and tracer dispersion models are used. In this work, we use a rich data set of atmospheric methane observations collected during an intensive airborne campaign to compare different methane emissions estimates from the Barnett Shale oil and natural gas production basin in Texas, USA. We estimate emissions based on a variety of different meteorological and dispersion models. Previous estimates of methane emissions from this region relied on a simple model (a mass balance analysis) as well as on ground-based measurements and statistical data analysis (an inventory). We find that in addition to meteorological model choice, the choice of tracer dispersion model also has a significant impact on the predicted down-wind methane concentrations given the same emissions field. The dispersion models tested often underpredicted the observed methane enhancements with significant variability (up to a factor of 3) between different models and between different days. We examine possible causes for this result and find that the models differ in their simulation of vertical dispersion, indicating that additional work is needed to evaluate and improve vertical mixing in the tracer dispersion models commonly used in regional trace gas flux inversions.

Intercomparison of atmospheric trace gas dispersion models: Barnett Shale case study

Greenhouse gas emissions mitigation requires understanding the dominant processes controlling fluxes of these trace gases at increasingly finer spatial and temporal scales. Trace gas fluxes can be estimated using a variety of approaches that translate observed atmospheric species mole fractions into fluxes or emission rates, often identifying the spatial and temporal characteristics of the emission sources as well. Meteorological models are commonly combined with tracer dispersion models to estimate fluxes using an inverse approach that optimizes emissions to best fit the trace gas mole fraction observations. One way to evaluate the accuracy of atmospheric flux estimation methods is to compare results from independent methods, including approaches in which different meteorological and tracer dispersion models are used. In this work, we use a rich data set of atmospheric methane observations collected during an intensive airborne campaign to compare different methane emissions estimates from the Barnett Shale oil and natural gas production basin in Texas, USA. We estimate emissions based on a variety of different meteorological and dispersion models. Previous estimates of methane emissions from this region relied on a simple model (a mass balance analysis) as well as on ground-based measurements and statistical data analysis (an inventory). We find that in addition to meteorological model choice, the choice of tracer dispersion model also has a significant impact on the predicted down-wind methane concentrations given the same emissions field. The dispersion models tested often underpredicted the observed methane enhancements with significant variability (up to a factor of 3) between different models and between different days. We examine possible causes for this result and find that the models differ in their simulation of vertical dispersion, indicating that additional work is needed to evaluate and improve vertical mixing in the tracer dispersion models commonly used in regional trace gas flux inversions.

Optimizing Smoke and Plume Rise Modeling Approaches at Local Scales

Heating from wildfires adds buoyancy to the overlying air, often producing plumes that vertically distribute fire emissions throughout the atmospheric column over the fire. The height of the rising wildfire plume is a complex function of the size of the wildfire, fire heat flux, plume geometry, and atmospheric conditions, which can make simulating plume rises difficult with coarser-scale atmospheric models. To determine the altitude of fire emission injection, several plume rise parameterizations have been developed in an effort estimate the height of the wildfire plume rise. Previous work has indicated the performance of these plume rise parameterizations has generally been mixed when validated against satellite observations. However, it is often difficult to evaluate the performance of plume rise parameterizations due to the significant uncertainties associated with fire input parameters such as fire heat fluxes and area. In order to reduce the uncertainties of fire input parameters, we applied an atmospheric modeling framework with different plume rise parameterizations to a well constrained prescribed burn, as part of the RxCADRE field experiment. Initial results found that the model was unable to reasonably replicate downwind smoke for cases when fire emissions were emitted at the surface and released at the top of the plume. However, when fire emissions were distributed below the plume top following a Gaussian distribution, model results were significantly improved.

Source Partitioning of Methane Emissions and its Seasonality in the U.S. Midwest

The methane (CH4) budget and its source partitioning are poorly constrained in the Midwestern United States. We used tall tower (185 m) aerodynamic flux measurements and atmospheric scale factor Bayesian inversions to constrain the monthly budget and to partition the total budget into natural (e.g., wetlands) and anthropogenic (e.g., livestock, waste, and natural gas) sources for the period June 2016 to September 2017. Aerodynamic flux observations indicated that the landscape was a CH4 source with a mean annual CH4 flux of +13.7 ± 0.34 nmol m-2 s-1 and was rarely a net sink. The scale factor Bayesian inversion analyses revealed a mean annual source of +12.3 ± 2.1 nmol m-2 s-1. Flux partitioning revealed that the anthropogenic source (7.8 ± 1.6 Tg CH4 yr-1) was 1.5 times greater than the bottom-up gridded United States Environmental Protection Agency inventory, in which livestock and oil/gas sources were underestimated by 1.8-fold and 1.3-fold, respectively. Wetland emissions (4.0 ± 1.2 Tg CH4 yr-1) were the second largest source, accounting for 34% of the total budget. The temporal variability of total CH4 emissions was dominated by wetlands with peak emissions occurring in August. In contrast, emissions from oil/gas and other anthropogenic sources showed relatively weak seasonality.

Tower-Based Greenhouse Gas Measurement Network Design---The National Institute of Standards and Technology North East Corridor Testbed

The North-East Corridor (NEC) Testbed project is the 3rd of three NIST (National Institute of Standards and Technology) greenhouse gas emissions testbeds designed to advance greenhouse gas measurements capabilities. A design approach for a dense observing network combined with atmospheric inversion methodologies is described. The Advanced Research Weather Research and Forecasting Model with the Stochastic Time-Inverted Lagrangian Transport model were used to derive the sensitivity of hypothetical observations to surface greenhouse gas emissions (footprints). Unlike other network design algorithms, an iterative selection algorithm, based on a k-means clustering method, was applied to minimize the similarities between the temporal response of each site and maximize sensitivity to the urban emissions contribution. Once a network was selected, a synthetic inversion Bayesian Kalman filter was used to evaluate observing system performance. We present the performances of various measurement network configurations consisting of differing numbers of towers and tower locations. Results show that an overly spatially compact network has decreased spatial coverage, as the spatial information added per site is then suboptimal as to cover the largest possible area, whilst networks dispersed too broadly lose capabilities of constraining flux uncertainties. In addition, we explore the possibility of using a very high density network of lower cost and performance sensors characterized by larger uncertainties and temporal drift. Analysis convergence is faster with a large number of observing locations, reducing the response time of the filter. Larger uncertainties in the observations implies lower values of uncertainty reduction. On the other hand, the drift is a bias in nature, which is added to the observations and, therefore, biasing the retrieved fluxes.

The Brazilian developments on the Regional Atmospheric Modeling System (BRAMS 5.2): an integrated environmental model tuned for tropical areas

We present a new version of the Brazilian developments on the Regional Atmospheric Modeling System where different previous versions for weather, chemistry and carbon cycle were unified in a single integrated software system. The new version also has a new set of state-of-the-art physical parameterizations and greater computational parallel and memory usage efficiency. Together with the description of the main features are examples of the quality of the transport scheme for scalars, radiative fluxes on surface and model simulation of rainfall systems over South America in different spatial resolutions using a scale-aware convective parameterization. Besides, the simulation of the diurnal cycle of the convection and carbon dioxide concentration over the Amazon Basin, as well as carbon dioxide fluxes from biogenic processes over a large portion of South America are shown. Atmospheric chemistry examples present model performance in simulating near-surface carbon monoxide and ozone in Amazon Basin and Rio de Janeiro megacity. For tracer transport and dispersion, it is demonstrated the model capabilities to simulate the volcanic ash 3-d redistribution associated with the eruption of a Chilean volcano. Then, the gain of computational efficiency is described with some details. BRAMS has been applied for research and operational forecasting mainly in South America. Model results from the operational weather forecast of BRAMS on 5 km grid spacing in the Center for Weather Forecasting and Climate Studies, INPE/Brazil, since 2013 are used to quantify the model skill of near surface variables and rainfall. The scores show the reliability of BRAMS for the tropical and subtropical areas of South America. Requirements for keeping this modeling system competitive regarding on its functionalities and skills are discussed. At last, we highlight the relevant contribution of this work on the building up of a South American community of model developers.

A multi-year estimate of methane fluxes in Alaska from CARVE atmospheric observations

Methane (CH₄) fluxes from Alaska and other arctic regions may be sensitive to thawing permafrost and future climate change, but estimates of both current and future fluxes from the region are uncertain. This study estimates CH₄ fluxes across Alaska for 2012-2014 using aircraft observations from the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE) and a geostatistical inverse model (GIM). We find that a simple flux model based on a daily soil temperature map and a static map of wetland extent reproduces the atmospheric CH₄ observations at the state-wide, multi-year scale more effectively than global-scale, state-of-the-art process-based models. This result points to a simple and effective way of representing CH₄ flux patterns across Alaska. It further suggests that contemporary process-based models can improve their representation of key processes that control fluxes at regional scales, and that more complex processes included in these models cannot be evaluated given the information content of available atmospheric CH₄ observations. In addition, we find that CH₄ emissions from the North Slope of Alaska account for 24% of the total statewide flux of 1.74 ± 0.44 Tg CH_{4 (}for May-Oct.). Contemporary global-scale process models only attribute an average of 3% of the total flux to this region. This mismatch occurs for two reasons: process models likely underestimate wetland area in regions without visible surface water, and these models prematurely shut down CH₄ fluxes at soil temperatures near 0°C. As a consequence, wetlands covered by vegetation and wetlands with persistently cold soils could be larger contributors to natural CH₄ fluxes than in process estimates. Lastly, we find that the seasonality of CH₄ fluxes varied during 2012-2014, but that total emissions did not differ significantly among years, despite substantial differences in soil temperature and precipitation; year-to-year variability in these environmental conditions did not affect obvious changes in total CH₄ fluxes from the state.

Continued emissions of carbon tetrachloride from the United States nearly two decades after its phaseout for dispersive uses

National-scale emissions of carbon tetrachloride (CCl4) are derived based on inverse modeling of atmospheric observations at multiple sites across the United States from the National Oceanic and Atmospheric Administration's flask air sampling network. We estimate an annual average US emission of 4.0 (2.0-6.5) Gg CCl4 y(-1) during 2008-2012, which is almost two orders of magnitude larger than reported to the US Environmental Protection Agency (EPA) Toxics Release Inventory (TRI) (mean of 0.06 Gg y(-1)) but only 8% (3-22%) of global CCl4 emissions during these years. Emissive regions identified by the observations and consistently shown in all inversion results include the Gulf Coast states, the San Francisco Bay Area in California, and the Denver area in Colorado. Both the observation-derived emissions and the US EPA TRI identified Texas and Louisiana as the largest contributors, accounting for one- to two-thirds of the US national total CCl4 emission during 2008-2012. These results are qualitatively consistent with multiple aircraft and ship surveys conducted in earlier years, which suggested significant enhancements in atmospheric mole fractions measured near Houston and surrounding areas. Furthermore, the emission distribution derived for CCl4 throughout the United States is more consistent with the distribution of industrial activities included in the TRI than with the distribution of other potential CCl4 sources such as uncapped landfills or activities related to population density (e.g., use of chlorine-containing bleach).

A method to quantitatively apportion pollutants at high spatial and temporal resolution: the Stochastic Lagrangian Apportionment Method (SLAM)

We introduce a method to quantify upwind contributions to concentrations of atmospheric pollutants. The Stochastic Lagrangian Apportionment Method (SLAM) carries out the following: (1) account for chemical transformations and depositional losses; (2) incorporate the effects of turbulent dispersion; (3) simulate the locations of the sources with high spatial and temporal resolution; and (4) minimize the impact from numerical diffusion. SLAM accomplishes these four features by using a time-reversed Lagrangian particle dispersion model and then simulating chemical changes forward in time, while tagging and keeping track of different sources. As an example of SLAM's application, we show its use in apportioning sources contributing to ammonia (NH3) and ammonium particulates (p-NH4(+)) at a site in southern Ontario, Canada. Agricultural emissions are seen to dominate contributions to NH3 and p-NH4(+) at the site. The source region of NH3 was significantly smaller than that of p-NH4(+), which covered numerous states of the American Midwest. The source apportionment results from SLAM were compared against those from zeroing-out individual sources ("brute force method"; BFM). The comparisons show SLAM to produce almost identical results as BFM for NH3, but higher concentrations of p-NH4(+), likely due to indirect effects that affect BFM. Finally, uncertainties in the SLAM approach and ways to address such shortcomings by combining SLAM with inverse methods are discussed.

Factors influencing surface CO2 variations in LPRU, Thailand and IESM, Philippines

Surface carbon dioxide concentrations were measured using a non-dispersive infrared carbon dioxide sensor at Lampang Rajabhat University from April to May 2013 and at the University of the Philippines-Diliman campus starting September 2013. Factors influencing the variations in these measurements were determined using multiple linear regression and a Lagrangian transport model. Air temperature and sea level pressure were the dominant meteorological factors that affect the CO2 variations. However, these factors are not enough. Surface CO2 flux and transboundary transport needs to be considered as well.

Oil spill hazard assessment using a reverse trajectory method for the Egadi marine protected area (Central Mediterranean Sea)

The Egadi Marine Protected Area (MPA) on the western side of the Sicily Channel (Central Mediterranean) is exposed to a high risk of oil pollution from the tanker routes connecting the eastern and western basins of the Mediterranean Sea. Areas where an oil spill would do most damage, and thus where surveillance should be concentrated, are identified in this study by Lagrangian tracers tracked backwards in time from points along the MPA perimeter using data spanning six years from 2006 to 2011. Results indicate that the areas where oil surveillance would be most beneficial are segments of the tanker routes south of Sicily (highly frequented) and north of Sicily (scarcely frequented), both extending about 150 miles from November to March and 100 miles in the other months. The third route, close to the Tunisian shore, is the most frequented by oil tankers but the threat period is limited to November and December.

Detecting fossil fuel emissions patterns from subcontinental regions using North American in situ CO2 measurements

The ability to monitor fossil fuel carbon dioxide (FFCO₂) emissions from subcontinental regions using atmospheric CO₂ observations remains an important but unrealized goal. Here we explore a necessary but not sufficient component of this goal, namely, the basic question of the detectability of FFCO₂ emissions from subcontinental regions. Detectability is evaluated by examining the degree to which FFCO₂ emissions patterns from specific regions are needed to explain the variability observed in high-frequency atmospheric CO₂ observations. Analyses using a CO₂ monitoring network of 35 continuous measurement towers over North America show that FFCO₂ emissions are difficult to detect during nonwinter months. We find that the compounding effects of the seasonality of atmospheric transport patterns and the biospheric CO₂ flux signal dramatically hamper the detectability of FFCO₂ emissions. Results from several synthetic data case studies highlight the need for advancements in data coverage and transport model accuracy if the goal of atmospheric measurement-based FFCO₂ emissions detection and estimation is to be achieved beyond urban scales.

KEY POINTS

Poor detectability of fossil fuel CO₂ emissions from subcontinental regionsDetectability assessed via attribution of emissions patterns in atmospheric dataLoss in detectability due to transport modeling errors and biospheric signal.

Anthropogenic emissions of methane in the United States

This study quantitatively estimates the spatial distribution of anthropogenic methane sources in the United States by combining comprehensive atmospheric methane observations, extensive spatial datasets, and a high-resolution atmospheric transport model. Results show that current inventories from the US Environmental Protection Agency (EPA) and the Emissions Database for Global Atmospheric Research underestimate methane emissions nationally by a factor of ∼1.5 and ∼1.7, respectively. Our study indicates that emissions due to ruminants and manure are up to twice the magnitude of existing inventories. In addition, the discrepancy in methane source estimates is particularly pronounced in the south-central United States, where we find total emissions are ∼2.7 times greater than in most inventories and account for 24 ± 3% of national emissions. The spatial patterns of our emission fluxes and observed methane-propane correlations indicate that fossil fuel extraction and refining are major contributors (45 ± 13%) in the south-central United States. This result suggests that regional methane emissions due to fossil fuel extraction and processing could be 4.9 ± 2.6 times larger than in EDGAR, the most comprehensive global methane inventory. These results cast doubt on the US EPA's recent decision to downscale its estimate of national natural gas emissions by 25-30%. Overall, we conclude that methane emissions associated with both the animal husbandry and fossil fuel industries have larger greenhouse gas impacts than indicated by existing inventories.