ARL Weekly News – May 8, 2020
HYSPLIT-based web app developed for United Nations’ locust tracking: NOAA issued a press release and web story regarding ARL’s development of a web-based, user friendly application enabling the UN Food and Agriculture Organization to more easily forecast and warn of historic numbers of desert locust swarms.
Enhanced Operational Model: ARL’s atmospheric chemistry team unanimously supported and advocated for the implementation of the National Weather Service (NWS) Global Ensemble Forecasting System Version 12 (GEFS-V12), which provides a vast improvement in aerosol science compared to the currently operational GEFS-V11. Team management includes Dr. Rick Saylor, who leads research in chemical and physical processes that inject and remove aerosol species between the surface and the atmosphere, and Dr. Pius Lee, who leads impact testing of chemical composition fed into the National Air Quality Forecasting Capability; the forecasting performance and research of which is spearheaded by ARL. Drs. Saylor and Lee contributed to the new system’s evaluation presentation to the NWS directors, who approved the implementation set to take place in September 2020.
Annual HYSPLIT Workshop: Registration for ARL’s 2020 HYSPLIT Workshop opened on May 4 and closed on May 7 when the total number of registrants reached the 500-person ceiling. There is now a wait list for this virtual training scheduled for June 22-25.
Drs. Hyuncheol Kim, Pius Lee, Rick Saylor, Youhua Tang, Daniel Tong and Barry Baker co-led former ARL colleague Dr. Li Pan’s effort to publish a landmark paper in wildfire emission evaluation titled “Evaluating a ﬁre smoke simulation algorithm in the National Air Quality Forecast Capability (NAQFC) by using multiple observation data sets during the Southeast Nexus (SENEX) ﬁeld campaign.” Recently published in Geoscientific Model Development, the paper concluded that the operational NAQFC was capable of capturing major wildfire signals and successfully transforming those signals to emission mass and heat fluxes; however, the lack of smoke plume treatment for intruding plumes from outside the forecast domain through the appropriate application of lateral chemical boundary conditions was a modeling deficiency.
Summary: In addition to conventional wildfire hotspot detection by real color images retrieved from satellites, this paper applied multiple tagging of independent pollutant species frequently found in wildfire smoke plumes, such as carbon monoxide and acetonitrile, to corroborate wildfire signals. This versatile methodology proved effective in reducing uncertainty in wildfire emission modeling when its results were extensively tested and analyzed against campaign data obtained in surface and airborne platforms.
Abstract: Multiple observation data sets – Interagency Monitoring of Protected Visual Environments (IMPROVE) network data, the Automated Smoke Detection and Tracking Algorithm (ASDTA), Hazard Mapping System (HMS) smoke plume shapefiles and aircraft acetonitrile (CH3CN) measurements from the NOAA Southeast Nexus (SENEX) field campaign – are used to evaluate the HMS–BlueSky–SMOKE (Sparse Matrix Operator Kernel Emission)–CMAQ (Community Multi-scale Air Quality Model) fire emissions and smoke plume prediction system. A similar configuration is used in the US National Air Quality Forecasting Capability (NAQFC). The system was found to capture most of the observed fire signals. Usage of HMS-detected fire hotspots and smoke plume information was valuable for deriving both fire emissions and forecast evaluation. This study also identified that the operational NAQFC did not include fire contributions through lateral boundary conditions, resulting in significant simulation uncertainties. In this study we focused both on system evaluation and evaluation methods. We discussed how to use observational data correctly to retrieve fire signals and synergistically use multiple data sets. We also addressed the limitations of each of the observation data sets and evaluation methods.
Citation: Pan, L., Kim, H., Lee, P., Saylor, R., Tang, Y., Tong, D., Baker, B., Kondragunta, S., Xu, C., Ruminski, M. G., Chen, W., Mcqueen, J., and Stajner, I.: Evaluating a fire smoke simulation algorithm in the National Air Quality Forecast Capability (NAQFC) by using multiple observation data sets during the Southeast Nexus (SENEX) field campaign, Geosci. Model Dev., 13, 2169–2184, https://doi.org/10.5194/gmd-13-2169-2020, 2020.
A manuscript titled “Using near-road observations of CO, NOy, and CO2 to investigate emissions from vehicles: Evidence for an impact of ambient temperature and specific humidity,” on which Xinrong Ren is a coauthor, was accepted for publication in Atmospheric Environment. In it, the authors compared vehicular emissions of carbon monoxide (CO) and nitrogen oxides (NOx) inferred from ambient measurements at a near-road (NR) site in the Baltimore-Washington, D.C. region to output from the U.S. Environmental Protection Agency’s (EPA) emission model. Emissions of CO and NOx observations at the NR site were examined for their sensitivity to ambient temperature and specific humidity. Temperature and humidity dependence were studied using all hours and various times of the day.
Abstract: Vehicles are a significant source of CO and NOx, two harmful pollutants and precursors to ozone formation. Previous studies have shown that emissions of NOx in the U.S. EPA’s National Emissions Inventory (NEI) are overestimated relative to observations in the summer and possibly for an annual average. Here we use measurements of CO, NOx, carbon dioxide (CO2), and meteorological variables collected at a near-road site along I-95 in Howard County, Maryland, during the cold months of 2016 and 2017, to infer ΔCO/ΔNOx, ΔCO2/ΔNOx, and ΔCO2/ΔCO emission ratios from vehicular running exhaust and their sensitivity to temperature and specific humidity. We also use aircraft observations of CO, NOx, and meteorological variables collected during the 2011 summertime DISCOVER-AQ campaign over the Baltimore-Washington region to analyze the impact of temperature and humidity on ΔCO/ΔNOx ratios, which integrate anthropogenic and biogenic sources in the urban area. Overall, we find a strong, statistically significant increase of 113% in ΔCO/ΔNOx and of 112% in ΔCO2/ΔNOx from −5 to 25°C at the I-95 NR site, indicating a decrease of approximately 50% in emissions of NOx, linked primarily to diesel-powered trucks. Temperature sensitivity of pollution control equipment on diesel vehicles may contribute to this trend. Results are robust when using several different techniques for calculating emission ratios. The specific humidity sensitivity is much weaker and cannot solely explain the trend with temperature. The aircraft data show a similar increase of 114% in ΔCO/ΔNOx from 25°C to 34°C, with a weaker sensitivity to specific humidity. In comparison to the NR observations, ΔCO/ΔNOx output from the MOtor Vehicle Emission Simulator (MOVES) with default settings, used to simulate mobile emissions for air quality models and in the NEI, showed a smaller increase for ΔCO/ΔNOx of 41% over the temperature range −5 to 24°C. The increase in ΔCO/ΔNOx from MOVES is due to an increase in emissions of CO by 23% and a decrease in emissions of NOx by 11% over −5 to 24°C, which is less than the observed decrease in NOx. Our study suggests that the overestimate in emissions of NOx in the NEI previously reported using summertime observations may be corrected in part by accounting for the temperature sensitivity of mobile NOx running emissions within MOVES. Future work will focus on improving MOVES by adjusting parameters controlling the impact of temperature and humidity on emissions to better represent the behavior of real-world vehicular emissions.