Accurate quantification of the sources and sinks of long-lived air pollutants is fundamental for effective emissions management, particularly in urban areas where emissions are generally more intense. Stakeholders commonly use so-called bottom-up methods to estimate emissions for urban areas. This type of emission accounting is typically carried out for annual totals, often with a latency of one or more years. Alternative methods that provide estimates with higher temporal resolution and lower latency could be helpful for stakeholders seeking targeted strategies to reduce emissions. A top-down urban emissions estimation system for the Washington, DC, and Baltimore, MD, metropolitan area, called the Urban Atmospheric Monitoring and Modeling System (Urban-AMMS), is being developed to provide accurate, up-to-date urban emissions data. Urban-AMMS has several components, including tower-based, aircraft, and mobile van measurements platforms, whose data are assimilated by the CarbonTracker-Lagrange analytical inverse model; an ensemble of HYSPLIT backward dispersion simulations driven by in-house high-resolution WRF simulations (spatial resolution of 1 km) enhanced with urban meteorological observations; biospheric models; and bottom-up inventories used for a prior estimate of emissions in the domain. The inversion system is tailored to account for the underlying variability in urban fluxes of an inert tracer (CO2) by solving for hourly fluxes and incorporating explicit spatiotemporal covariance of prior errors, as well as high-resolution source-receptor sensitivities estimated by WRF-HYSPLIT. Here, we present an overview of Urban-AMMS, including initial results and sensitivity analyses to investigate the effects of prior spatial aggregation, background handling, and the temporal covariance of prior errors. Numerical experiments show improvements in estimates of urban surface fluxes at both the city and grid cell scales. Still, the reliability of inverse fluxes depends on prior uncertainty, as observed in previous studies. These findings provide critical insights for the inverse estimation of long-lived air pollutants in complex urban environments.

Abstract Accurate quantification of hazardous material releases in cities is critical for emergency managers aiming to mitigate health impacts and mortality in the population. Here, a study on the development and evaluation of a high‐resolution (1‐km, 1‐hr) urban‐scale atmospheric inverse modeling system for the Washington, DC, and Baltimore, MD, metropolitan area (DCBA) is presented to advance surface flux quantification in urban and industrial areas where the potential for a hazardous release is high. The system employs an inert tracer as a proof of concept for January 2019 and builds upon HYSPLIT atmospheric transport and dispersion simulations driven by high‐resolution Weather Research and Forecasting (WRF) model simulations that ingest urban meteorological observations, coupled with the CarbonTracker‐Lagrange (CT‐L) inverse model tailored to account for hourly spatiotemporal fluxes. The system assimilates tower‐based tracer observations from the National Institute of Standards and Technology (NIST) Northeast Corridor Urban Test Bed and utilizes the 1‐km Vulcan and 4‐km GRA2PES bottom‐up inventories as prior knowledge of the tracer fluxes. Numerical experiments using synthetic and actual data reveal that the system significantly improves the quantification of flux strength (mean flux) and total mass released across the urban cores of Washington, DC, and Baltimore, MD. Hourly flux variations are fairly well resolved when originating from a broad territory (city scale), while the accuracy of source retrieval improves when flux estimates are aggregated to coarser spatial resolutions (e.g., 4 km). This work has important implications for estimating hazardous releases from urban areas for emergency response and hazard analysis.

NOAA has over 50 years of experience monitoring global atmospheric levels of greenhouse gasses (GHG) emitted to the atmosphere by human activities and natural sources. This includes most notably measurements of atmospheric abundances of CO2 and other long-lived GHGs at its Global Greenhouse Gas Reference Network (GGGRN). The goal of GGGRN is to provide measurements of GHGs and their large-scale spatial and temporal distributions as precisely and accurately as possible to be able to determine spatial gradients in GHGs and inform changes in emissions and sinks. Success at meeting this goal requires long-term continuity of measurements and measurement quality. To that end, NOAA is largely responsible for the World Meteorological Organization (WMO) standard for CO2 and several other GHG measurements, providing a solid foundation for well-calibrated global atmospheric GHG measurements. The NOAA carbon monitoring covers both the atmosphere and the oceans and its unique multi-platform approach to observing GHGs, includes the ocean observing capabilities of air-sea carbon fluxes and pCO2 measurements, among other key assets. While these networks have been instrumental in providing a basic understanding of the carbon cycle, there are many gaps that need to be filled over the next decade to assess interannual variability in both natural and anthropogenic sources and sinks of GHGs. This presentation will highlight the latest developments in NOAA carbon monitoring, including the development of new planforms, networks, and tracers for observing atmospheric and ocean GHGs gradients and processes driving these gradients. Innovative research supported by NOAA has leveraged long-term GHG monitoring to accelerate the development of global GHG models, and advanced GHG research and accounting on urban, regional, and global scales. NOAA’s GHG capabilities span measurements, process research, modeling, data assimilation and data products such as the Annual Greenhouse Gas Index (AGGI) as well as future climate projections for IPCC reports and the National Climate Assessment. NOAA uniquely includes research and service capabilities under one roof. Decades of experience in carbon cycle research, including GHG monitoring, modeling and data assimilation provide the ideal foundation to accelerate national and international efforts for carbon measurement, monitoring, reporting and verification (MMRV) in support of climate mitigation.

The newly established United States Greenhouse Gas Center (U.S. GHG Center) is a multi-agency partnership between the National Aeronautics and Space Administration (NASA), the Environmental Protection Agency (EPA), the National Oceanic and Atmospheric Administration (NOAA) and the National Institute of Standards and Technology (NIST) that aims to accelerate the production and delivery of actionable, trusted greenhouse gas (GHG) information from the federal government and non-public sector to a variety of users through a coordinated data system, reflecting transparency and open source science principles in both data and methods. The US GHG Center acts as an enabler of collaboration with networks of interagency, international, intergovernmental and private sector partners to increase confidence in setting, assessing, and meeting climate change mitigation goals, with a preliminary focus on carbon dioxide and methane. The US GHG Center is also a critical element in the implementation of the “National Strategy to Advance an Integrated US Greenhouse Gas Measurement, Monitoring, and Information System”. Initial focus areas include 1) Gridded anthropogenic greenhouse gas emissions, 2) Natural sources and sinks, and 3) New Observations for tracking large emission events. The US GHG Center web portal includes a prototype data catalogue, exploratory data analysis capabilities, a collaborative science environment for data analysis and exploration, as well as an interactive visual interface for storytelling. Examples of products currently available on the GHG Center portal include methane and carbon dioxide concentration anomalies and emissions from airborne and space-based instruments, including from NASA’s Earth Surface Mineral Dust Source Investigation (EMIT) imaging spectrometer in orbit on the International Space Station, EPA’s gridded U.S. anthropogenic methane greenhouse gas inventory data, gridding methodologies and visualizations, NOAA’s Observation Package (ObsPack) data products that bring together atmospheric greenhouse gas observations from a variety of sampling platforms, as well as multi-model land flux and ecosystem exchange estimates.

For over 75 years, the National Oceanic and Atmospheric Administration’s Air Resources Laboratory (NOAA ARL) has been at the forefront of federal meteorological and climate research. As the Special Projects Section (SPS) of the U.S. Weather Bureau (USWB), the laboratory pioneered the development of atmospheric trajectory modeling, initially used in studies related to nuclear weapons following World War II. Model development was guided by observations following weapons tests, assisted by later experiments using a wide variety of atmospheric tracers. Today’s familiar Gaussian plume dispersion model, previously in nascent form, was developed and promoted with ARL research, as was the much later and widely used HYSPLIT model. Much of ARL’s early research was focused on the challenges presented by the complex terrain surrounding nuclear installations, often addressed with high-spatial-resolution meteorological measurements, atmospheric tracers, and site-specific models. ARL has since extended boundary layer research to increasingly complex landscapes, such as forests, agricultural lands, and urban areas, and has expanded its research scope to air quality, weather, and climate applications based on the knowledge and experience developed throughout its long history. Examples of these research endeavors include the establishment of the U.S. Climate Reference Network, fundamental contributions to the development of the National Air Quality Forecast Capability, and foundational participation in the National Atmospheric Deposition Program. ARL looks forward to continuing to refine scientific understanding from field experiments, including coupling ground-based experimentation with modeling, and sustained observations, in order to facilitate the transfer of knowledge into practical applications of societal relevance.

In 2015 and 2016, atmospheric transport modeling challenges were conducted in the context of the Comprehensive Nuclear-Test-Ban Treaty (CTBT) verification, however, with a more limited scope with respect to emission inventories, simulation period and number of relevant samples (i.e., those above the Minimum Detectable Concentration (MDC)) involved. Therefore, a more comprehensive atmospheric transport modeling challenge was organized in 2019. Stack release data of Xe-133 were provided by the Institut National des Radioéléments/IRE (Belgium) and the Canadian Nuclear Laboratories/CNL (Canada) and accounted for in the simulations over a three (mandatory) or six (optional) months period. Best estimate emissions of additional facilities (radiopharmaceutical production and nuclear research facilities, commercial reactors or relevant research reactors) of the Northern Hemisphere were included as well. Model results were compared with observed atmospheric activity concentrations at four International Monitoring System (IMS) stations located in Europe and North America with overall considerable influence of IRE and/or CNL emissions for evaluation of the participants' runs. Participants were prompted to work with controlled and harmonized model set-ups to make runs more comparable, but also to increase diversity. It was found that using the stack emissions of IRE and CNL with daily resolution does not lead to better results than disaggregating annual emissions of these two facilities taken from the literature if an overall score for all stations covering all valid observed samples is considered. A moderate benefit of roughly 10% is visible in statistical scores for samples influenced by IRE and/or CNL to at least 50% and there can be considerable benefit for individual samples. Effects of transport errors, not properly characterized remaining emitters and long IMS sampling times (12-24 h) undoubtedly are in contrast to and reduce the benefit of high-quality IRE and CNL stack data. Complementary best estimates for remaining emitters push the scores up by 18% compared to just considering IRE and CNL emissions alone. Despite the efforts undertaken the full multi-model ensemble built is highly redundant. An ensemble based on a few arbitrary runs is sufficient to model the Xe-133 background at the stations investigated. The effective ensemble size is below five. An optimized ensemble at each station has on average slightly higher skill compared to the full ensemble. However, the improvement (maximum of 20% and minimum of 3% in RMSE) in skill is likely being too small for being exploited for an independent period.

The discovery of atmospheric micro(nano)plastic transport and ocean–atmosphere exchange points to a highly complex marine plastic cycle, with negative implications for human and ecosystem health. Yet, observations are currently limited. In this Perspective, we quantify the processes and fluxes of the marine-atmospheric micro(nano)plastic cycle, with the aim of highlighting the remaining unknowns in atmospheric micro(nano)plastic transport. Between 0.013 and 25 million metric tons per year of micro(nano)plastics are potentially being transported within the marine atmosphere and deposited in the oceans. However, the high uncertainty in these marine-atmospheric fluxes is related to data limitations and a lack of study intercomparability. To address the uncertainties and remaining knowledge gaps in the marine-atmospheric micro(nano)plastic cycle, we propose a future global marine-atmospheric micro(nano)plastic observation strategy, incorporating novel sampling methods and the creation of a comparable, harmonized and global data set. Together with long-term observations and intensive investigations, this strategy will help to define the trends in marine-atmospheric pollution and any responses to future policy and management actions. Atmospheric transport of microplastics could be a major source of plastic pollution to the ocean, yet observations currently remain limited. This Perspective quantifies the known budgets of the marine-atmospheric micro(nano)plastic cycle and proposes a future global observation strategy.

The latest operational National Air Quality Forecast Capability (NAQFC) has been advanced to use the Community Multiscale Air Quality (CMAQ) model (version 5.3.1) with the CB6r3 (Carbon Bond 6 revision 3) AERO7 (version 7 of the aerosol module) chemical mechanism and is driven by the Finite-Volume Cubed-Sphere (FV3) Global Forecast System, version 16 (GFSv16). This update has been accomplished via the development of the meteorological preprocessor, NOAA-EPA Atmosphere–Chemistry Coupler (NACC), adapted from the existing Meteorology–Chemistry Interface Processor (MCIP). Differing from the typically used Weather Research and Forecasting (WRF) CMAQ system in the air quality research community, the interpolation-based NACC can use various meteorological outputs to drive the CMAQ model (e.g., FV3-GFSv16), even though they are on different grids. In this study, we compare and evaluate GFSv16-CMAQ and WRFv4.0.3-CMAQ using observations over the contiguous United States (CONUS) in summer 2019 that have been verified with surface meteorological and AIRNow observations. During this period, the Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) field campaign was performed, and we compare the two models with airborne measurements from the NASA DC-8 aircraft. The GFS-CMAQ and WRF-CMAQ systems show similar performance overall with some differences for certain events, species and regions. The GFSv16 meteorology tends to have a stronger diurnal variability in the planetary boundary layer height (higher during daytime and lower at night) than WRF over the US Pacific coast, and it also predicted lower nighttime 10 m winds. In summer 2019, the GFS-CMAQ system showed better surface ozone (O3) than WRF-CMAQ at night over the CONUS domain; however, the models' fine particulate matter (PM2.5) predictions showed mixed verification results: GFS-CMAQ yielded better mean biases but poorer correlations over the Pacific coast. These results indicate that using global GFSv16 meteorology with NACC to directly drive CMAQ via interpolation is feasible and yields reasonable results compared to the commonly used WRF approach.

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.

On March 11th , 2020, the World Health Organization (WHO) characterized the COVID-19 respiratory disease caused by the coronavirus (SARS-CoV-2) as a world wide pandemic which led to a massive slowdown in anthropogenic activity as people attempted to "shelter in place". In response to this slowdown NOAA's Global Monitoring Lab (GML), in collaboration with the National Institute of Standards and Technology (NIST), University of Michigan, University of Maryland, Stony Brook University and NOAA's Chemical Science and Atmospheric Resource Laboratories, launched a campaign to measure CO2, CH4, and CO emissions from five major cities along the northeast corridor of the US (Washington, D.C., Baltimore, MD, Philadelphia, PA, New York, NY, and Boston, MA). The month-long campaign which lasted from April 16 to May 16 of 2020 mirrored a campaign that was completed exactly two years prior in April and May of 2018 and which enabled direct comparison of CO2, CH4, CO emissions from these five cities before and during SARS-CoV-2. In this work, we used a Bayesian multi-resolution tiered inversion framework to quantify the CO2, CH4 and CO emissions from these urban areas. We used the HYSPLIT atmospheric transport and dispersion model to calculate the sensitivity of our aircraft observations to surface fluxes (footprints) using three meteorological drivers (NAM, ERA5 and a custom WRF); using three driver models allowed us to account for uncertainties in the transport. To account for biospheric influences on atmospheric CO2, we used a year-specific VPRM simulation that allowed us to isolate the fossil-fuel contribution and solve for it alone. In addition, we also solved for total CO2 and show that not accounting for biogenic activity in lower latitude urban areas could have led to an overestimation of the observed reduction due to biogenic flux differences between the two years. Results show that systematic reductions in CO2 and CO emissions for the five urban areas occurred in April 2020 with signs of recovery in May 2020, which had larger emissions than April 2020. The observed reductions and evolution are consistent with bottom-up estimations based on mobility metrics, which showed the lowest mobility in April with progressive recovery in May. Fuel use from tax records indicates similar reductions. In addition, we show that changes are not homogeneous in space within the urban metropolitan areas and that CO2 and CO emissions reductions are collocated, showing the largest drops in urban centers and roads. While CO2 and CO estimated reductions and evolution are systematic in all cities, CH4 does not show a clear reduction or consistent pattern among cities during the COVID-19 lock-downs. In fact, all the measured changes for CH4 were lower than the standard errors of the differences, implying that the observed changes in CH4 are not significant. Last, we note that since the same prior emissions, constant in time, were used in all the inversions, the anomalous decrease in posterior emissions and subsequent recovery in CO2 and CO observed during the COVID-19 lock-down period are driven by the atmospheric observations and not by temporal changes in the prior emissions.

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 nearsurface 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 NOxlimited (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.

The Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model is a state-of-the-science atmospheric dispersion model that is developed and maintained at the National Oceanic Atmospheric Administration’s (NOAA) Air Resources Laboratory (ARL). In the early 2000s, HYSPLIT served as the starting point for development of the Stochastic Time-Inverted Lagrangian Transport (STILT) model that emphasizes backward-in-time dispersion simulations to determine source regions of receptors. STILT continued its separate development and gained a wide user base. Since STILT was built on a now outdated version of HYSPLIT and lacks long-term institutional support to maintain the model, incorporating STILT features into HYSPLIT allows these features to stay up to date. This paper describes the STILT features incorporated into HYSPLIT, which include: a new vertical interpolation algorithm for WRF derived meteorological input files, a detailed algorithm for estimating boundary layer height, a new turbulence parameterization, a vertical Lagrangian timescale that varies in time and space, a complex dispersion algorithm, and two new convection schemes. An evaluation of these new features was performed using tracer release data from the Cross Appalachian Tracer Experiment and the Across North America Tracer Experiment. Results show the dispersion module from STILT, which takes up to double the amount of time to run, is less dispersive in the vertical and in better agreement with observations than the existing HYSPLIT option. The other new modeling features from STILT were not consistently statistically different than existing HYSPLIT options. Forward-time simulations from the new model were also compared against backward-time equivalents and found to be statistically comparable to one another.

Smoke forecasts have been challenged by high uncertainty in fire emission estimates. We develop an inverse modeling system, the HYSPLIT-based Emissions Inverse Modeling System for wildfires (or HEIMS-fire), that estimates wildfire emissions from the transport and dispersion of smoke plumes as measured by satellite observations. A cost function quantifies the differences between model predictions and satellite measurements, weighted by their uncertainties. The system then minimizes this cost function by adjusting smoke sources until wildfire smoke emission estimates agree well with satellite observations. Based on HYSPLIT and Geostationary Operational Environmental Satellite (GOES) Aerosol/Smoke Product (GASP), the system resolves smoke source strength as a function of time and vertical level. Using a wildfire event that took place in the southeastern United States during November 2016, we tested the system's performance and its sensitivity to varying configurations of modeling options, including vertical allocation of emissions and spatial and temporal coverage of constraining satellite observations. Compared with currently operational BlueSky emission predictions, emission estimates from this inverse modeling system outperform in both reanalysis (21 out of 21 d; −27 % average root-mean-square-error change) and hindcast modes (29 out of 38 d; −6 % average root-mean-square-error change) compared with satellite observed smoke mass loadings.

Operational air quality predictions produced by NOAA/National Weather Service include predictions of ozone, fine particulate matter (PM2.5) and wildfire smoke for the United States and predictions of windblown dust for the contiguous 48 states. Prediction maps are distributed at https://airquality.weather.gov/ and as a web service at https://idpgis.ncep.noaa.gov/arcgis/rest/services/NWS_Forecasts_Guidance_Warnings. Community Multiscale Air Quality modeling system (CMAQ) used for ozone and PM2.5 predictions was most recently updated in December 2018, when a unified bias-correction procedure was introduced for ozone and PM2.5. Wildfire smoke dispersion is operationally predicted using the Hybrid Single Particle Lagrangian Integrated Trajectory Model (HYSPLIT). Wildfire smoke dispersion is included in version 4 of the Eulerian High Resolution Rapid Refresh (HRRR) model that is being evaluated for operational implementation. One of the largest uncertainties for prediction of air quality over the U.S. involves specification of wildfire smoke emissions. Different smoke emission approaches used in these systems will be discussed.NOAA is testing ozone and PM2.5 predictions extended from 48 to 72 hours. Global aerosol predictions, which include biomass burning aerosols, are being tested for inclusion in one member of the Global Ensemble Forecast System (GEFS) to replace the current standalone global aerosol prediction system. An overview of this testing will be presented.NOAA's operational air quality predictions contribute to protection of lives and health in the United States. These predictions are used by state and local air quality forecasters to issue official air quality forecasts for their respective areas. General public accesses NOAA's hourly predictions directly at https://airquality.weather.gov/. These predictions are also used in the applications about wildfire and health as well as in smoke vulnerability assessments lead by our partners at the U. S. Environmental Protection Agency (EPA) and Centers for Disease Control and Prevention (CDC).

Atmospheric mercury species (including gaseous elemental mercury (GEM), gaseous oxidized mercury (GOM) and particulate-bound mercury (PBM)), trace pollutants (including O3, SO2, CO, NO, NOY and black carbon), and meteorological parameters have been continuously monitored since 2007 at an Atmospheric Mercury Network (AMNet) site located on the northern coast of the Gulf of Mexico at the Grand Bay National Estuarine Research Reserve (NERR) in Moss Point, Mississippi. For the data collected between 2007 and 2018, the average concentrations and standard deviations were 1.39 ± 0.22 ng m-3 for GEM, 5.1 ± 10.2 pg m-3 for GOM, 5.9 ± 13.0 pg m-3 for PBM, and 309 ± 407 ng m-2 wk-1 for mercury wet deposition, with interannual trends of -0.009 ng m-3 yr-1 for GEM, -0.36 pg m-3 yr-1 for GOM, 0.18 pg m-3 yr-1 for PBM, and 2.8 ng m-2 wk-1 yr-1 for mercury wet deposition. The trends are statistically significant for GEM and GOM, but not statistically significant for PBM and mercury wet deposition. Diurnal variation of GEM shows lower concentrations in the early morning due to GEM depletion likely due to plant uptake in high humidity events and slight elevation during the day likely due to downward mixing of higher concentrations of GEM in the air aloft to the surface. Seasonal variation of GEM shows higher levels in winter and spring and lower levels in summer and fall. Diurnal variations of both GOM and PBM show broad peaks in the afternoon likely due to photochemical oxidation of GEM. Seasonally, PBM measurements exhibit higher levels in winter and early spring and lower levels in summer, while GOM measurements show high levels in late spring/early summer and late fall and low levels in winter. The seasonal variation of mercury wet deposition shows higher values in summer and lower values in winter due to higher precipitation amounts in summer than in winter. As expected, anticorrelation between Hg wet deposition and the sum of GOM and PBM but positive correlation between Hg wet deposition and rainfall were observed. Correlation among GOM, ozone, and SO2 suggests two possible GOM sources: direct emissions and photochemical oxidation of GEM with the possible influence of boundary dynamics and seasonal variability. This study indicates that the monitoring site, which is located in a coastal environment of the Gulf of Mexico, might experience impacts from mercury sources that are both local and regional in nature.