Development and evaluation of an advanced National Air Quality Forecasting Capability using the NOAA Global Forecast System version 16.

A new dynamical core, known as the Finite-Volume Cubed-Sphere (FV3) and developed at both NASA and NOAA, is used in NOAA's Global Forecast System (GFS) and in limited-area models for regional weather and air quality applications. NOAA has also upgraded the operational FV3GFS to version 16 (GFSv16), which includes a number of significant developmental advances to the model configuration, data assimilation, and underlying model physics, particularly for atmospheric composition to weather feedback. Concurrent with the GFSv16 upgrade, we couple the GFSv16 with the Community Multiscale Air Quality (CMAQ) model to form an advanced version of the National Air Quality Forecasting Capability (NAQFC) that will continue to protect human and ecosystem health in the US. Here we describe the development of the FV3GFSv16 coupling with a "state-of-the-science" CMAQ model version 5.3.1. The GFS-CMAQ coupling is made possible by the seminal version of the NOAA-EPA Atmosphere-Chemistry Coupler (NACC), which became a major piece of the next operational NAQFC system (i.e., NACC-CMAQ) on 20 July 2021. NACC-CMAQ has a number of scientific advancements that include satellite-based data acquisition technology to improve land cover and soil characteristics and inline wildfire smoke and dust predictions that are vital to predictions of fine particulate matter (PM2.5) concentrations during hazardous events affecting society, ecosystems, and human health. The GFS-driven NACC-CMAQ model has significantly different meteorological and chemical predictions compared to the previous operational NAQFC, where evaluation of NACC-CMAQ shows generally improved near-surface ozone and PM2.5 predictions and diurnal patterns, both of which are extended to a 72 h (3 d) forecast with this system.

Impacts of the COVID-19 economic slowdown on ozone pollution in the U.S.

In this work, we use observations and experimental emissions in a version of NOAA's National Air Quality Forecasting Capability to show that the COVID-19 economic slowdown led to disproportionate impacts on near-surface ozone concentrations across the contiguous U.S. (CONUS). The data-fusion methodology used here includes both U.S. EPA Air Quality System ground and the NASA Aura satellite Ozone Monitoring Instrument (OMI) NO2 observations to infer the representative emissions changes due to the COVID-19 economic slowdown in the U.S. Results show that there were widespread decreases in anthropogenic (e.g., NOx) emissions in the U.S. during March-June 2020, which led to widespread decreases in ozone concentrations in the rural regions that are NOx-limited, but also some localized increases near urban centers that are VOC-limited. Later in June-September, there were smaller decreases, and potentially some relative increases in NOx emissions for many areas of the U.S. (e.g., south-southeast) that led to more extensive increases in ozone concentrations that are partly in agreement with observations. The widespread NOx emissions changes also alters the O3 photochemical formation regimes, most notably the NOx emissions decreases in March-April, which can enhance (mitigate) the NOx-limited (VOC-limited) regimes in different regions of CONUS. The average of all AirNow hourly O3 changes for 2020-2019 range from about +1 to -4 ppb during March-September, and are associated with predominantly urban monitoring sites that demonstrate considerable spatiotemporal variability for the 2020 ozone changes compared to the previous five years individually (2015-2019). The simulated maximum values of the average O3 changes for March-September range from about +8 to -4 ppb (or +40 to -10%). Results of this work have implications for the use of widespread controls of anthropogenic emissions, particularly those from mobile sources, used to curb ozone pollution under the current meteorological and climate conditions in the U.S.

Author Correction: Global and regional trends of atmospheric sulfur.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Wintertime CO2, CH4, and CO Emissions Estimation for the Washington, DC-Baltimore Metropolitan Area Using an Inverse Modeling Technique.

Since greenhouse gas mitigation efforts are mostly being implemented in cities, the ability to quantify emission trends for urban environments is of paramount importance. However, previous aircraft work has indicated large daily variability in the results. Here we use measurements of CO2, CH4, and CO from aircraft over 5 days within an inverse model to estimate emissions from the DC-Baltimore region. Results show good agreement with previous estimates in the area for all three gases. However, aliasing caused by irregular spatiotemporal sampling of emissions is shown to significantly impact both the emissions estimates and their variability. Extensive sensitivity tests allow us to quantify the contributions of different sources of variability and indicate that daily variability in posterior emissions estimates is larger than the uncertainty attributed to the method itself (i.e., 17% for CO2, 24% for CH4, and 13% for CO). Analysis of hourly reported emissions from power plants and traffic counts shows that 97% of the daily variability in posterior emissions estimates is explained by accounting for the sampling in time and space of sources that have large hourly variability and, thus, caution must be taken in properly interpreting variability that is caused by irregular spatiotemporal sampling conditions.

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.

Global and regional trends of atmospheric sulfur.

The profound changes in global SO2 emissions over the last decades have affected atmospheric composition on a regional and global scale with large impact on air quality, atmospheric deposition and the radiative forcing of sulfate aerosols. Reproduction of historical atmospheric pollution levels based on global aerosol models and emission changes is crucial to prove that such models are able to predict future scenarios. Here, we analyze consistency of trends in observations of sulfur components in air and precipitation from major regional networks and estimates from six different global aerosol models from 1990 until 2015. There are large interregional differences in the sulfur trends consistently captured by the models and observations, especially for North America and Europe. Europe had the largest reductions in sulfur emissions in the first part of the period while the highest reduction came later in North America and East Asia. The uncertainties in both the emissions and the representativity of the observations are larger in Asia. However, emissions from East Asia clearly increased from 2000 to 2005 followed by a decrease, while in India a steady increase over the whole period has been observed and modelled. The agreement between a bottom-up approach, which uses emissions and process-based chemical transport models, with independent observations gives an improved confidence in the understanding of the atmospheric sulfur budget.

International challenge to model the long-range transport of radioxenon released from medical isotope production to six Comprehensive Nuclear-Test-Ban Treaty monitoring stations.

After performing a first multi-model exercise in 2015 a comprehensive and technically more demanding atmospheric transport modelling challenge was organized in 2016. Release data were provided by the Australian Nuclear Science and Technology Organization radiopharmaceutical facility in Sydney (Australia) for a one month period. Measured samples for the same time frame were gathered from six International Monitoring System stations in the Southern Hemisphere with distances to the source ranging between 680 (Melbourne) and about 17,000 km (Tristan da Cunha). Participants were prompted to work with unit emissions in pre-defined emission intervals (daily, half-daily, 3-hourly and hourly emission segment lengths) and in order to perform a blind test actual emission values were not provided to them. Despite the quite different settings of the two atmospheric transport modelling challenges there is common evidence that for long-range atmospheric transport using temporally highly resolved emissions and highly space-resolved meteorological input fields has no significant advantage compared to using lower resolved ones. As well an uncertainty of up to 20% in the daily stack emission data turns out to be acceptable for the purpose of a study like this. Model performance at individual stations is quite diverse depending largely on successfully capturing boundary layer processes. No single model-meteorology combination performs best for all stations. Moreover, the stations statistics do not depend on the distance between the source and the individual stations. Finally, it became more evident how future exercises need to be designed. Set-up parameters like the meteorological driver or the output grid resolution should be pre-scribed in order to enhance diversity as well as comparability among model runs.

Recent increase of surface particulate matter concentrations in the Seoul Metropolitan Area, Korea.

Recent changes of surface particulate matter (PM) concentration in the Seoul Metropolitan Area (SMA), South Korea, are puzzling. The long-term trend of surface PM concentration in the SMA declined in the 2000s, but since 2012 its concentrations have tended to incline, which is coincident with frequent severe hazes in South Korea. This increase puts the Korean government's emission reduction efforts in jeopardy. This study reports that interannual variation of surface PM concentration in South Korea is closely linked with the interannual variations of wind speed. A 12-year (2004-2015) regional air quality simulation was conducted over East Asia (27-km) and over South Korea (9-km) to assess the impact of meteorology under constant anthropogenic emissions. Simulated PM concentrations show a strong negative correlation (i.e. R = -0.86) with regional wind speed, implying that reduced regional ventilation is likely associated with more stagnant conditions that cause severe pollutant episodes in South Korea. We conclude that the current PM concentration trend in South Korea is a combination of long-term decline by emission control efforts and short-term fluctuation of regional wind speed interannual variability. When the meteorology-driven variations are removed, PM concentrations in South Korea have declined continuously even after 2012.

Large Salt Dust Storms Follow a 30-Year Rainfall Cycle in the Mar Chiquita Lake (Córdoba, Argentina).

Starting in 2006, a new source of intense salt dust storms developed in Mar Chiquita (Córdoba, Argentina), the largest saline lake in South America. Storms originate from vast mudflats left by a 30-year expansion-retreat cycle of the lake due to changes in the regional rainfall regime. The annual frequency of salt dust storms correlated with the size of the salt mudflats. Events were restricted to the coldest months, and reached up to 800 km from the source. Occurrence of dust storms was associated with specific surface colors and textures easily identifiable in satellite images. High-emission surfaces were characterized by the presence of sodium sulfate hydrous/anhydrous crystals (mirabilite and thenardite), and a superficial and variable water table, which may result in the periodic development of a characteristic "fluffy" surface derived from salt precipitation-dissolution processes. HYSPLIT model simulation estimates a deposition maximum near the sources (of about 2.5 kg/ha/yr), and a decreasing trend from the emission area outwards, except for the relative secondary maximum modeled over the mountain ranges in southern Bolivia and northern Argentina due to an orographic effect. The 2009 total deposition of salt dust generated in Mar Chiquita was estimated at 6.5 million tons.

International challenge to predict the impact of radioxenon releases from medical isotope production on a comprehensive nuclear test ban treaty sampling station.

The International Monitoring System (IMS) is part of the verification regime for the Comprehensive Nuclear-Test-Ban-Treaty Organization (CTBTO). At entry-into-force, half of the 80 radionuclide stations will be able to measure concentrations of several radioactive xenon isotopes produced in nuclear explosions, and then the full network may be populated with xenon monitoring afterward. An understanding of natural and man-made radionuclide backgrounds can be used in accordance with the provisions of the treaty (such as event screening criteria in Annex 2 to the Protocol of the Treaty) for the effective implementation of the verification regime. Fission-based production of (99)Mo for medical purposes also generates nuisance radioxenon isotopes that are usually vented to the atmosphere. One of the ways to account for the effect emissions from medical isotope production has on radionuclide samples from the IMS is to use stack monitoring data, if they are available, and atmospheric transport modeling. Recently, individuals from seven nations participated in a challenge exercise that used atmospheric transport modeling to predict the time-history of (133)Xe concentration measurements at the IMS radionuclide station in Germany using stack monitoring data from a medical isotope production facility in Belgium. Participants received only stack monitoring data and used the atmospheric transport model and meteorological data of their choice. Some of the models predicted the highest measured concentrations quite well. A model comparison rank and ensemble analysis suggests that combining multiple models may provide more accurate predicted concentrations than any single model. None of the submissions based only on the stack monitoring data predicted the small measured concentrations very well. Modeling of sources by other nuclear facilities with smaller releases than medical isotope production facilities may be important in understanding how to discriminate those releases from releases from a nuclear explosion.

Modeling and evaluation of urban pollution events of atmospheric heavy metals from a large Cu-smelter.

Metal smelting and processing are highly polluting activities that have a strong influence on the levels of heavy metals in air, soil, and crops. We employ an atmospheric transport and dispersion model to predict the pollution levels originated from the second largest Cu-smelter in Europe. The model predicts that the concentrations of copper (Cu), zinc (Zn), and arsenic (As) in an urban area close to the Cu-smelter can reach 170, 70, and 30 ng m−3, respectively. The model captures all the observed urban pollution events, but the magnitude of the elemental concentrations is predicted to be lower than that of the observed values; ~300, ~500, and ~100 ng m−3 for Cu, Zn, and As, respectively. The comparison between model and observations showed an average correlation coefficient of 0.62 ± 0.13. The simulation shows that the transport of heavy metals reaches a peak in the afternoon over the urban area. The under-prediction in the peak is explained by the simulated stronger winds compared with monitoring data. The stronger simulated winds enhance the transport and dispersion of heavy metals to the regional area, diminishing the impact of pollution events in the urban area. This model, driven by high resolution meteorology (2 km in horizontal), predicts the hourly-interval evolutions of atmospheric heavy metal pollutions in the close by urban area of industrial hotspot.

Acute hypoglycemia results in reduced cortical neuronal injury in the developing IUGR rat.

BACKGROUND: Hypoglycemia (HG) is common in intrauterine growth restricted (IUGR) neonates. In normally grown (NG) neonatal rats, acute HG causes neuronal injury in the brain; the cerebral cortex is more vulnerable than the hippocampus (HPC). We hypothesized that the IUGR brain is less vulnerable to HG-induced injury while preserving regional variation in vulnerability. METHODS: We induced IUGR via bilateral uterine artery ligation on gestational day 19 (term 22 d) rats. On postnatal day 14, insulin-induced HG of equivalent severity and duration (blood glucose < 40 mg/dl for 240 min) was produced in IUGR and NG (IUGR/HG and NG/HG). Neuronal injury in the cortex and HPC was quantified 6-72 h later using Fluoro-Jade B (FJB) histochemistry. The mRNA expression of monocarboxylate transporters, MCT1 and MCT2, and glucose transporters, GLUT1 and GLUT3, was determined using quantitative PCR. RESULTS: There were fewer FJB-positive (FJB+) cells in the cortex of IUGR/HG; no difference was observed in FJB+ cells in HPC. Core body temperature was lower in IUGR/HG compared with NG/HG. MCT2 expression was increased in the IUGR cortex. CONCLUSION: HG-induced neuronal injury is decreased in the cortex of the developing IUGR brain. Adaptations including systemic hypothermia and enhanced delivery of alternative substrates via MCT2 might protect against HG-induced neuronal injury in IUGR.

Hyperglycemia accentuates and ketonemia attenuates hypoglycemia-induced neuronal injury in the developing rat brain.

BACKGROUND: Prolonged hypoglycemia leads to brain injury, despite treatment with 10% dextrose. Whether induction of hyperglycemia or ketonemia achieves better neuroprotection is unknown. Hyperglycemia is neuroprotective in other brain injuries during development; however, it worsens hypoglycemia-induced injury in the adult brain via poly(ADP-ribose)polymerase-1 (PARP-1) overactivation. METHODS: Three-week-old rats were subjected to insulin-induced hypoglycemia and treated with 10% dextrose or 50% dextrose. Neuronal injury, PARP-1, and brain-derived neurotrophic factor (BDNF) III/TrkB/p75(NTR) expressions were determined. In the second experiment, ketonemia was induced by administering ß-hydroxybutyrate during hypoglycemia and its effect on neuronal injury was compared with those conventionally treated using 10% dextrose. RESULTS: Both 10 and 50% dextrose administration led to hyperglycemia (50% dextrose > 10% dextrose). Compared with the 10% dextrose group, neuronal injury was greater in the 50% dextrose group and was accompanied by PARP-1 overactivation. BDNF III and p75(NTR), but not TrkBFL, mRNA expressions were upregulated. Neuronal injury was less severe in the rats subjected to ketonemia, compared with those conventionally treated using 10% dextrose. CONCLUSION: Hyperglycemia accentuated hypoglycemia-induced neuronal injury, likely via PARP-1 overactivation. Although BDNF was upregulated, it was not neuroprotective and potentially exaggerated injury by binding to p75(NTR) receptor. Conversely, ketonemia during hypoglycemia attenuated neuronal injury.

Emissions from pre-Hispanic metallurgy in the South American atmosphere.

Metallurgical activities have been undertaken in northern South America (NSA) for millennia. However, it is still unknown how far atmospheric emissions from these activities have been transported. Since the timing of metallurgical activities is currently estimated from scarce archaeological discoveries, the availability of reliable and continuous records to refine the timing of past metal deposition in South America is essential, as it provides an alternative to discontinuous archives, as well as evidence for global trace metal transport. We show in a peat record from Tierra del Fuego that anthropogenic metals likely have been emitted into the atmosphere and transported from NSA to southern South America (SSA) over the last 4200 yrs. These findings are supported by modern time back-trajectories from NSA to SSA. We further show that apparent anthropogenic Cu and Sb emissions predate any archaeological evidence for metallurgical activities. Lead and Sn were also emitted into the atmosphere as by-products of Inca and Spanish metallurgy, whereas local coal-gold rushes and the industrial revolution contributed to local contamination. We suggest that the onset of pre-Hispanic metallurgical activities is earlier than previously reported from archaeological records and that atmospheric emissions of metals were transported from NSA to SSA.

Apparent signal of competition limiting diversification after ecological transitions from marine to freshwater habitats.

Adaptive radiations are typically triggered when a lineage encounters a significant range of open niche space (ecological opportunity), stemming from colonisation of new areas, extinction of competitors or key innovations. The most well-known of these is the colonisation of new areas, through either dispersal into new regions or the invasion of novel ecological regimes. One aspect of ecological opportunity that has rarely been studied, however, is the extent to which pre-existent competitors act to limit diversification in newly colonised adaptive zones. Herein, we show that in multiple geographically independent invasions of freshwaters by marine Sea Catfishes (Ariidae), rates of both morphological disparification and lineage diversification are inversely related to the presence and diversity of other freshwater fish lineages. Only in one region (Australia-New Guinea) with an otherwise depauperate freshwater fauna, has an ariid invasion gained any substantial traction. This is true at both regional and community scales, suggesting that competitive constraints may be an important factor regulating adaptive radiation.

The New England Air Quality Forecasting Pilot Program: development of an evaluation protocol and performance benchmark.

The National Oceanic and Atmospheric Administration recently sponsored the New England Forecasting Pilot Program to serve as a "test bed" for chemical forecasting by providing all of the elements of a National Air Quality Forecasting System, including the development and implementation of an evaluation protocol. This Pilot Program enlisted three regional-scale air quality models, serving as prototypes, to forecast ozone (O3) concentrations across the northeastern United States during the summer of 2002. A suite of statistical metrics was identified as part of the protocol that facilitated evaluation of both discrete forecasts (observed versus modeled concentrations) and categorical forecasts (observed versus modeled exceedances/nonexceedances) for both the maximum 1-hr (125 ppb) and 8-hr (85 ppb) forecasts produced by each of the models. Implementation of the evaluation protocol took place during a 25-day period (August 5-29), utilizing hourly O3 concentration data obtained from over 450 monitors from the U.S. Environment Protection Agency's Air Quality System network.