Improving predictability of high-ozone episodes through dynamic boundary conditions, emission refresh and chemical data assimilation during the Long Island Sound Tropospheric Ozone Study (LISTOS) field campaign

Although air quality in the United States has improved remarkably in the past decades, ground-level ozone (O3) often rises in exceedance of the national ambient air quality standard in nonattainment areas, including the Long Island Sound (LIS) and its surrounding areas. Accurate prediction of high-ozone episodes is needed to assist government agencies and the public in mitigating harmful effects of air pollution. In this study, we have developed a suite of potential forecast improvements, including dynamic boundary conditions, rapid emission refresh and chemical data assimilation, in a 3 km resolution Community Multiscale Air Quality (CMAQ) modeling system. The purpose is to evaluate and assess the effectiveness of these forecasting techniques, individually or in combination, in improving forecast guidance for two major air pollutants: surface O3 and nitrogen dioxide (NO2). Experiments were conducted for a high-O3 episode (28–29 August 2018) during the Long Island Sound Tropospheric Ozone Study (LISTOS) field campaign, which provides abundant observations for evaluating model performance. The results show that these forecast system updates are useful in enhancing the capability of this 3 km forecasting model with varying effectiveness for different pollutants. For O3 prediction, the most significant improvement comes from the dynamic boundary conditions derived from the NOAA operational forecast system, National Air Quality Forecast Capability (NAQFC), which increases the correlation coefficient (R) from 0.81 to 0.93 and reduces the root mean square error (RMSE) from 14.97 to 8.22 ppbv, compared to that with the static boundary conditions (BCs). The NO2 from all high-resolution simulations outperforms that from the operational 12 km NAQFC simulation, regardless of the BCs used, highlighting the importance of spatially resolved emission and meteorology inputs for the prediction of short-lived pollutants. The effectiveness of improved initial concentrations through optimal interpolation (OI) is shown to be high in urban areas with high emission density. The influence of OI adjustment, however, is maintained for a longer period in rural areas, where emissions and chemical transformation make a smaller contribution to the O3 budget than that in high-emission areas. Following the assessment of individual updates, the forecasting system is configured with dynamic boundary conditions, optimal interpolation of initial concentrations and emission adjustment, to simulate a high-ozone episode during the 2018 LISTOS field campaign. The newly developed forecasting system significantly reduces the bias of surface NO2 prediction. When compared with the NASA Langley GeoCAPE Airborne Simulator (GCAS) vertical column density (VCD), this system is able to reproduce the NO2 VCD with a higher correlation (0.74), lower normalized mean bias (40 %) and normalized mean error (61 %) than NAQFC (0.57, 45 % and 76 %, respectively). The 3 km system captures magnitude and timing of surface O3 peaks and valleys better. In comparison with lidar, O3 profile variability of the vertical O3 is captured better by the new system (correlation coefficient of 0.71) than by NAQFC (correlation coefficient of 0.54). Although the experiments are limited to one pollution episode over the Long Island Sound, this study demonstrates feasible approaches to improve the predictability of high-O3 episodes in contemporary urban environments.

Abiotic Aggregation of Organic Matter in Coastal and Estuarine Waters: Cases in the Eastern Long Island Sound, USA

Organic aggregates, which formed from small particles and dissolved material, were chemically characterized in the Long Island Sound coastal waters. In this study, six aggregation experiments were conducted on low-salinity samples (the Thames River, CT, USA; salinity of 6.3–6.8) and high-salinity samples (the coast of Avery Point, CT, USA; salinity of 21.4–26.7). Water samples were incubated on a roller table for two days under dark conditions to generate laboratory-made aggregates. Particulate organic carbon (POC) concentrations increased 5–39% after two days of rolling. A higher POC increase occurred in low-salinity samples. The concentrations of neutral aldoses and amino acids, as well as their C- and N-yields, decreased during the experiments (except for particulate hydrolysable amino acid in low-salinity samples), while bacterial abundance increased 50–476%, indicating microbial degradation of biologically labile organic matter. Particulate hydrolysable amino acid was preferentially preserved in P-limited systems. An enrichment factor analysis showed the preferential microbial degradation of particulate hydrolysable neutral aldose and glucose appeared as the most labile aldose. The increase in bulk POC and the decrease in the fraction of labile organic carbon (neutral aldose and amino acid) in the particulate phase resulted in an accumulation of uncharacterized (presumably more refractory) particulate organic matter.

Underwater Sound Propagation Modeling in a Complex Shallow Water Environment

Three-dimensional (3D) effects can profoundly influence underwater sound propagation in shallow-water environments, hence, affecting the underwater soundscape. Various geological features and coastal oceanographic processes can cause horizontal reflection, refraction, and diffraction of underwater sound. In this work, the ability of a parabolic equation (PE) model to simulate sound propagation in the extremely complicated shallow water environment of Long Island Sound (United States east coast) is investigated. First, the 2D and 3D versions of the PE model are compared with state-of-the-art normal mode and beam tracing models for two idealized cases representing the local environment in the Sound: (i) a 2D 50-m flat bottom and (ii) a 3D shallow water wedge. After that, the PE model is utilized to model sound propagation in three realistic local scenarios in the Sound. Frequencies of 500 and 1500 Hz are considered in all the simulations. In general, transmission loss (TL) results provided by the PE, normal mode and beam tracing models tend to agree with each other. Differences found emerge with (1) increasing the bathymetry complexity, (2) expanding the propagation range, and (3) approaching the limits of model applicability. The TL results from 3D PE simulations indicate that sound propagating along sand bars can experience significant 3D effects. Indeed, for the complex shallow bathymetry found in some areas of Long Island Sound, it is challenging for the models to track the interference effects in the sound pattern. Results emphasize that when choosing an underwater sound propagation model for practical applications in a complex shallow-water environment, a compromise will be made between the numerical model accuracy, computational time, and validity.

Motor-Tanker Coimbra - The Million Gallon Gamble

ABSTRACT In January of 1942, the M/T COIMBRA, a British flag vessel, carrying 64,800 barrels of lubricating oil and fuel oil was torpedoed by the German submarine U-123. It sank 26 NM south of Shinnecock Inlet, NY. The vessel was classified by the National Oceanic and Atmospheric Administration (NOAA) as a Remediation of Underwater Legacy Environmental Threats (RULET) vessel and was ranked the second highest pollution threat in the First Coast Guard District area of responsibility and the highest for U. S. Coast Guard Sector Long Island Sound. In 2015, Sector Long Island Sound started receiving pollution reports from the National Environmental Satellite Data and Information Service (NESDIS) via NOAA of anomalies in the vicinity of the wreck location of M/T COIMBRA. The anomalies were later confirmed by U.S. Coast Guard overflights and surface sheen sampling as oil. The Federal On-Scene Coordinator (FOSC) determined that M/T COIMBRA posed a substantial threat to the environment and opened the Oil Spill Liability Trust Fund to complete an assessment and execute an oil removal operation from the vessel that was deteriorating on the bottom of the Atlantic Ocean for 77 years. The vast number of unknowns, government regulations, technological challenges, varying weather patterns, diverse marine fauna, and lack of historical records about non-U.S. flagged vessels complicated the response to this pollution threat. Establishing a diverse team of professionals comprised of federal, academic, and state specialists, a detailed statement of work was created, and a contractor was chosen to complete objectives established by the FOSC. As a result of the operation, the U.S. Coast Guard determined the following: - Types and quantity of oil onboard the subject vessel- Best practices for the oil removal from an historic wreck- The impacts of salt water on the riveted hull structures- Challenges of the oil removal from various historical ship construction types A total of 81 out of 87 wrecks on the RULET database remain unassessed and unexplored in the United States alone. RULET wrecks and other historical polluting wrecks around the world may present a significant threat to the environment. The COIMBRA project answers some of the questions that will make future cases of these polluting historical wrecks more cost effective and less time consuming. In the best-case scenario this project will serve as an example for establishment of the framework for future projects of this nature.

Improving predictability of high ozone episodes through dynamic boundary conditions, emission refresh and chemical data assimilation during the Long Island Sound Tropospheric Ozone Study (LISTOS) field campaign

Although air quality in the United States improved remarkably in the past decades, ground-level ozone (O3) rises often in exceedance of the national ambient air quality standard in nonattainment areas, including the Long Island Sound (LIS) and its surrounding areas. Accurate prediction of high ozone episodes is needed to assist government agencies and the public in mitigating harmful effects of air pollution. In this study, we have developed a suite of potential forecast improvements, including dynamic boundary conditions, rapid emission refresh and chemical data assimilation, in a 3 km resolution Community Multi-scale Air Quality (CMAQ) modeling system. The purpose is to evaluate and assess the effectiveness of these forecasting techniques, individually or in combination, in improving forecast guidance for two major air pollutants: surface O3 and nitrogen dioxide (NO2). Experiments were conducted for a high O3 episode (August 28–29, 2018) during the Long Island Sound Tropospheric Ozone Study (LISTOS) field campaign, which provides abundant observations for evaluating model performance. The results show that these forecast system updates are useful in enhancing the capability of the forecasting model with varying effectiveness for different pollutants. For O3 prediction, the most significant improvement comes from the dynamic boundary conditions derived from NOAA National Air Quality Forecast Capability (NAQFC), which increases the correlation coefficient (R) from 0.81 to 0.93 and reduces the Root Mean Square Error (RMSE) from 14.97 ppbv to 8.22 ppbv, compared to that with the static boundary conditions. The NO2 from all high-resolution simulations outperforms that from the operational 12 km NAQFC simulation, highlighting the importance of spatially resolved emission and meteorology inputs for the prediction of short-lived pollutants. The effectiveness of improved initial concentrations through optimal interpolation (OI) is shown to be high in urban areas with high emission density. The influence of OI adjustment, however, is maintained for a longer period in rural areas where emissions and chemical transformation make a smaller contribution to the O3 budget than that in high emission areas. Following the assessment of individual forecast system updates, the forecasting system is configured with dynamic boundary conditions, optimal interpolation of initial concentrations, and emission adjustment, to simulate the high ozone episode over the Long Island Sound region. The newly developed forecasting system significantly reduces the bias of surface NO2 concentration. When compared with the NASA Langley GeoCAPE Airborne Simulator (GCAS) vertical column density (VCD), the new system is able to reproduce the NO2 VCD with a higher correlation (0.74), lower normalized mean bias (40 %) and normalized mean error (61 %) than NAQFC (0.57, 45 % and 76 %, respectively). The new system captures magnitude and timing of surface O3 peaks and valleys better. In comparison with LIDAR O3 profile variability of the vertical O3 is captured better by the new system (correlation coefficient of 0.71) than by NAQFC (correlation coefficient of 0.54). Although the experiments are limited to one pollution episode over the Long Island Sound, this study demonstrates feasible approaches to improve the predictability of high O3 episodes in contemporary urban environments.