DOCSLIB.ORG

Responses to Significant Comments on the State and Tribal Designation Recommendations for the 2015 Ozone National Ambient Air Quality Standards (NAAQS)

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Responses to Significant Comments on the State and Tribal Designation Recommendations for the 2015 Ozone National Ambient Air Quality Standards (NAAQS) Responses to Significant Comments on the State and Tribal Designation Recommendations for the 2015 Ozone National Ambient Air Quality Standards (NAAQS) Docket Number EPA-HQ-OAR-2017-0548 U.S. Environmental Protection Agency April 2018 Table of Contents 1.0 Introduction ....................................................................................................................................... 4 2.0 Background ....................................................................................................................................... 4 3.0 Responses to Significant Comments on the State and Tribal Designation Recommendations for the 2015 Ozone NAAQS .................................................................................................................................... 5 3.1 General Issues ............................................................................................................................... 5 3.1.1 Super-Regional Areas ........................................................................................................... 5 3.1.2 Exceptional Events .............................................................................................................. 11 3.1.3 Effective Date ..................................................................................................................... 11 3.1.4 NAAQS Implementation ..................................................................................................... 12 3.1.5 Air Quality Data .................................................................................................................. 12 3.1.6 Nonattainment Boundaries .................................................................................................. 12 3.1.7 The Term “Nearby” ............................................................................................................ 12 3.1.8 Other Options and the Ozone Transport Commission ........................................................ 12 3.2. Area-Specific Issues .................................................................................................................... 13 3.2.1 EPA Region I ...................................................................................................................... 13 3.2.2. EPA Region II ..................................................................................................................... 13 3.2.3. EPA Region III.................................................................................................................... 13 3.2.4. EPA Region IV ................................................................................................................... 19 3.2.5. EPA Region V ..................................................................................................................... 19 3.2.6. EPA Region VI ................................................................................................................... 37 3.2.7. EPA Region VII .................................................................................................................. 42 3.2.8. EPA Region VIII ................................................................................................................. 42 3.2.9. EPA Region IX ................................................................................................................... 63 3.2.10. EPA Region X ..................................................................................................................... 66 3.2.11. Multi-State Areas ................................................................................................................ 67 3.3 Comments Not Requiring a Response from EPA ....................................................................... 81 3.3.1 NAAQS Related .................................................................................................................. 81 3.3.2 Comments on Unrelated Programs ..................................................................................... 82 2 List of Acronyms CAA Clean Air Act CAMx Comprehensive Air Quality Model with Extensions CBSA Core Based Statistical Areas CFR Code of Federal Regulations CMSA Combined Metropolitan Statistical Area CSA Combined Statistical Area CSPAR Cross-state Air Pollution Rule DFW Dallas-Fort Worth, Texas EE Exceptional Events EGU Electric Generating Unit EPA Environmental Protection Agency FR Federal Register HGB Houston-Galveston-Brazoria, Texas HYSPLIT Hybrid Single Particle Lagrangian Integrated Trajectory Model IEPA Illinois Environmental Protection Agency I/M (Vehicle) Inspection and Maintenance LAER Lowest Achievable Emission Rate MACT Maximum Achievable Control Technology MDE Maryland Department of the Environment MPO Metropolitan Planning Organization MSA Metropolitan Statistical Area NESHAP National Emission Standards for Hazardous Air Pollutants NAA Nonattainment Area NAAQS National Ambient Air Quality Standard NEI National Emissions Inventory NFR Notice of Final Rulemaking NOx Oxides of Nitrogen NSPS New Source Performance Standards NSR New Source Review NYC New York City NYMA New York Metropolitan (Nonattainment) Area OAQPS EPA Office of Air Quality Planning and Standards OMB Office of Management and Budget OTR Ozone Transport Region PADEP Pennsylvania Department of Environmental Protection PPB Parts Per Billion PPM Parts Per Million PSD Prevention of Significant Deterioration QA/QC Quality Assurance/Quality Control RACT Reasonably Available Control Technology SIP State Implementation Plan TCEQ Texas Commission on Environmental Quality TPY Tons per Year TSD Technical Support Document VMT Vehicle Miles Traveled VOC Volatile Organic Compounds 3 1.0 Introduction This document, together with the preamble to the final rule, and the Technical Support Documents (TSDs) for the designations, presents the responses of the Environmental Protection Agency (EPA) to the significant comments we received on our proposed designations. The responses presented in this document are intended to augment the responses to comments that appear in the preamble to the final rule and the TSD or to address comments not discussed in those documents. 2.0 Background On October 1, 2015, the EPA promulgated revised primary and secondary ozone national ambient air quality standards (NAAQS (80 FR 6592, October 26, 2015)). In that action, the EPA strengthened both standards to a level of 0.070 parts per million (ppm), while retaining their indicators, averaging times, and forms. The EPA revised the ozone standards based on an integrated assessment of an extensive body of new scientific evidence, which substantially strengthens our knowledge regarding ozone-related health and welfare effects, the results of exposure and risk analyses, the advice of the Clean Air Scientific Advisory Committee and consideration of public comments. The revised primary standard provides increased protection for children, older adults and people with asthma or other lung diseases, and other at-risk populations against an array of adverse health effects including lung function, increased respiratory symptoms and pulmonary inflammation and asthma exacerbations; effects that contribute to emergency department visits or hospital admissions; and mortality. The revised secondary standard provides protection of natural forests from adverse growth- related effects and is expected to provide increased protection from other effects of potential public welfare significance, including crop yield loss and visible foliar injury. On November 6, 2017, the EPA issued final designations for the 2015 NAAQS for ozone for most areas in the United States (U.S.). Specifically, the Agency found that most areas in the country met the standards and designated those areas, including 2,646 counties, two tribal areas and five territories, “attainment/unclassifiable.” This represented about 85 percent of the counties in the U.S. The EPA also designated three counties in the state of Washington as “unclassifiable”, because there was not enough data to calculate a 3-year ozone design value. On December 22, 2017, the EPA responded to state and tribal recommendations by indicating the anticipated area designations for the portions of the country not already designated for the 2015 ozone standards. These responses started a 120-day period for states and tribes to provide additional information before the EPA determines the final designations. The EPA also opened a 30-day comment period for the public to provide input on these designations before they are finalized. Following are summaries of significant comments received on the 2015 ozone designation recommendations and the EPA’s responses to those comments. 4 3.0 Responses to Significant Comments on the State and Tribal Designation Recommendations for the 2015 Ozone NAAQS The following sections address the state, tribal, and public comments received by the EPA on the state and tribal ozone designation recommendations for the 2015 Ozone NAAQS. Comment summaries and responses are presented below. Comments and responses addressing general issues are presented first followed by area-specific comments and responses. The EPA has provided additional detail for some nonattainment areas in the TSD for that area. Commenters can find the TSDs in the electronic docket for this action (www.regulations,gov, docket number EPA-HQ-OAR-2017-0548) and at the EPA’s Ozone Designations Web Page (www.epa.gov/ozone-designations). 3.1 General Issues
Load more

Recommended publications
  • 2Nd Quarter Publications Conference Presentations & Invited Talks Other
    NOAA Air Resources Laboratory Quarterly Activity Report FY2019 Quarter 2 (January-February-March 2019) Contents: DISPERSION AND BOUNDARY LAYER Project Sagebrush Field Research Division (FRD) Tracer Program Boundary Layer Research Wind Forecast Improvement Program (WFIP) NOAA/INL Mesonet HYSPLIT Radiological (HYRad) Dispersion System Emergency Operations Center (EOC) 2019 HYSPLIT Workshop Atmospheric Turbulence & Diffusion Division (ATDD) – Unmanned Aircraft System (UAS) Program Office Work Boundary-Layer Meteorology Decision Support for Flight Planning Interagency Meeting Ashfall Project Consequence Assessment for the Nevada National Security Site (NNSS) SORD Mesonet Support to DOE/NNSA NNSS Projects and Experiments ATMOSPHERIC CHEMISTRY AND DEPOSITION National Air Quality Forecasting Capability (NAQFC) Upgrade Major Upgrade on Point Sources and Characterization Research Sampling Flights CLIMATE OBSERVATIONS AND ANALYSES U.S. Climate Reference Network (USCRN) ARL 2nd Quarter Publications Conference Presentations & Invited Talks Other DISPERSION AND BOUNDARY LAYER Project Sagebrush The manuscript “Effects of low-level jets on near-surface turbulence and wind direction changes in the stable boundary layer,” submitted to the Quarterly Journal of the Royal Meteorological Society late in 2018, was rejected for publication. We are now in the process of studying the relationships between wind direction changes and stability parameters under two contrasting turbulence regimes (presence of low-level jet versus no gradient of vertical wind). We will incorporate these new results into this manuscript before re-submitting it for publishing. ([email protected], [email protected]) Field Research Division (FRD) Tracer Program The division has continued testing a newer refrigerant called R-1234ze to determine whether it has any promise as an atmospheric tracer.
    [Show full text]
  • CICS Annual Report 2017
    Volume II CICS Annual Report 2017 Figure. 1. Validation of the Blended Rain Rate product relative to MRMS over CONUS. The bottom panels show data latency, observing satellite, scatterplot, and validation statistics. AMSR2/GCOM-W was recently incorporated into the bRR product. Correlation coefficient, root mean squared difference (RMSD), probability of detection (POD), and false alarm rate (FAR) are calculated for each time step. The most recent analyses are available online (http://cics.umd.edu/pmeyers/brr/). In addition to the bRR monitoring, routine monitoring of the stand- alone AMSR2 rain rate product is available through the CICS-MD International Precipitation Working Group website (http://cics.umd.edu/ipwg/NPPAMSR2.html). The effects of field of view (FOV) size on validation metrics were examined to create a more equitable evaluation of AMSR2 versus the Advanced Technology Microwave Sounder (ATMS). The ATMS FOVs range from 15km to 60km in diameter, relative to 5km for AMSR2, so direct comparisons of RMSD favor ATMS because lower rain rates, spatial smooth- ing, and reduced variance. Resampling AMSR2 to similar ATMS FOV sizes reduces RMSD by 50%, and performance metrics for both sensors are comparable. Efforts were underway to reduce high false alarm rates over radiometrically cold land surfaces. The leg- acy GPROF2010 empirical screening procedures are incapable of discriminating between rain and cold semi-arid surfaces, so methods to reduce the false alarm rate have been explored. Initial results suggest applying a probabilistic cloud-free determination scheme (Turk et al. 2016) dramatically improves the Critical Success Index (CSI) and Heidke Skill Score (HSS), metrics of overall detection skill.
    [Show full text]
  • Quantifying Uncertainty of Ensemble Transport and Dispersion Simulations Using HYSPLIT Daniel W
    Air Force Institute of Technology AFIT Scholar Theses and Dissertations Student Graduate Works 3-22-2019 Quantifying Uncertainty of Ensemble Transport and Dispersion Simulations Using HYSPLIT Daniel W. Bazemore Follow this and additional works at: https://scholar.afit.edu/etd Part of the Atmospheric Sciences Commons, and the Fluid Dynamics Commons Recommended Citation Bazemore, Daniel W., "Quantifying Uncertainty of Ensemble Transport and Dispersion Simulations Using HYSPLIT" (2019). Theses and Dissertations. 2197. https://scholar.afit.edu/etd/2197 This Thesis is brought to you for free and open access by the Student Graduate Works at AFIT Scholar. It has been accepted for inclusion in Theses and Dissertations by an authorized administrator of AFIT Scholar. For more information, please contact [email protected]. Quantifying Uncertainty of Ensemble Transport and Dispersion Simulations Using HYSPLIT THESIS Daniel W. Bazemore, Captain, USAF AFIT-ENP-MS-19-M-068 DEPARTMENT OF THE AIR FORCE AIR UNIVERSITY AIR FORCE INSTITUTE OF TECHNOLOGY Wright-Patterson Air Force Base, Ohio DISTRIBUTION STATEMENT A. APPROVED FOR PUBLIC RELEASE; DISTRIBUTION UNLIMITED. The views expressed in this thesis are those of the author and do not reflect the official policy or position of the United States Air Force, Department of Defense, or the United States Government. This material is declared a work of the U.S. Government and is not subject to copyright protection in the United States. AFIT-ENP-MS-19-M-068 QUANTIFYING UNCERTAINTY OF ENSEMBLE TRANSPORT AND DISPERSION SIMULATIONS USING HYSPLIT THESIS Presented to the Faculty Department of Engineering Physics Graduate School of Engineering and Management Air Force Institute of Technology Air University Air Education and Training Command In Partial Fulfillment of the Requirements for the Degree of Master of Science in Atmospheric Science Daniel W.
    [Show full text]
  • Study of Saharan Dust Influence on Pm10 Measures in Sicily from 2013 to 2015
    STUDY OF SAHARAN DUST INFLUENCE ON PM10 MEASURES IN SICILY FROM 2013 TO 2015 A. Cuspilici, P. Monforte, M.A.Ragusa * Department of Mathematics and Computer Science Catania University Corresponding author *[email protected] Abstract Nowadays, particulate matter, especially that with small dimension as PM 10 , PM 2.5 and PM 1, is the air quality indicator most commonly associated with a number of adverse health effects. In this paper it is analyzed the impact that a natural event, such as the transport of Saharan dust, can have on increasing the particulate matter concentration in Sicily. Consulting the data of daily PM 10 concentration, acquired by air quality monitoring network belonging to “Agenzia Regionale Protezione dell’Ambiente” (Environmental Protection Regional Agency), it was possible to analyze the trend from 2013 to 2015. The days, in which the limit value was exceeded, were subjected to combined analysis. It was based on three models: interpretations of the air masses back-trajectories, using the atmospheric model HYSPLIT (HYbrid Single-Particle Lagrangian Integrated trajectory); on the calculation of the concentration on the ground and at high altitude particulate applying DREAM model (Dust REgional atmospheric model) and on the calculation of the concentration of mineral aerosols according to the atmospheric optical thickness (AOT) applying NAAPS model (Navy Aerosol Analysis and Prediction System).The daily limit value exceedances were attributed to the transport of Saharan dust events exclusively when the three models were in agreement with each other. Identifying the natural events, it was possible to quantify the contribution of the Saharan dust and consequently the reduction of the exceedances number.
    [Show full text]
  • Source-Receptor Modeling Using High Resolution Wrf Meteorological Fields and the Hysplit Model to Assess Mercury Pollution Over the Mississippi Gulf Coast Region
    SOURCE-RECEPTOR MODELING USING HIGH RESOLUTION WRF METEOROLOGICAL FIELDS AND THE HYSPLIT MODEL TO ASSESS MERCURY POLLUTION OVER THE MISSISSIPPI GULF COAST REGION Anjaneyulu Yerramilli 1,* , Venkata Bhaskar Rao Dodla 1 , Hari Prasad Dasari 1 , Challa Venkata Srinivas 1, Francis Tuluri 2, Julius M. Baham 1, John H. Young 1, Robert Hughes 1, Chuck Patrick 1, Mark G. Hardy 2, Shelton J. Swanier 1, Mark.D.Cohen 3 , Winston Luke 3 , Paul Kelly 3 and Richard Artz 3 1 Trent Lott Geospatial Visualization Research Centre, Jackson State University, Jackson MS 39217,USA 2College of Science, Engineering &Technology, Jackson State University, Jackson MS 39217,USA 3NOAA Air Resouce Laboratory, NOAA/ARL, 1315 East WestHighway ,Silver Spring, Maryland 20910-3282, USA The HYSPLIT atmospheric dispersion model, ABSTRACT driven by the output from WRF model, was used to obtain the Lagrangian path of trajectories from the The Mississippi Gulf Coastal region is NERR observation station. Backward trajectories were environmentally sensitive due to multiple air pollution generated for every hour during May 4-7, corresponding problems originating as a consequence of several to the episode and for one day before and after the developmental activities such as oil and gas refineries, episode. These back trajectories are used in conjunction operation of thermal power plants, and mobile-source with a regional mercury emissions inventory to identify pollution. Mercury is known to be a potential air pollutant the potential sources of mercury contributing to the high in the region apart from SOX, NOX,CO and Ozone. concentrations observed. Throughout the study, Mercury contamination in water bodies and other trajectory results using high-resolution WRF ecosystems due to deposition of atmospheric mercury is meteorological data fields are compared with considered a serious environmental concern.
    [Show full text]
  • Screening Approach for Estimating Uptake of Dioxins from Areal
    Screening Level Assessment of Risks Due to Dioxin Emissions from Burning Oil from the BP Deepwater Horizon Gulf of Mexico Spill John Schaum1*, Mark Cohen3, Steven Perry2, Richard Artz3, Roland Draxler3, Jeffrey B. Frithsen1, David Heist2, Matthew Lorber1, and Linda Phillips1 1U.S. EPA, Office of Research and Development, Washington, DC 2U.S. EPA, Office of Research and Development, RTP, NC 3NOAA, Air Resources Laboratory, Silver Spring, MD *Corresponding author e-mail: [email protected]: phone (703) 347- 8623; fax (703) 347- 8690 ABSTRACT Between April 28 and July 19 of 2010, the US Coast Guard conducted in situ oil burns as one approach used for the management of oil spilled after the explosion and subsequent sinking of the BP Deepwater Horizon platform in the Gulf of Mexico. The purpose of this paper is to describe a screening level assessment of the exposures and risks posed by the dioxin emissions from these fires. Using upper estimates for the oil burn emission factor, modeled air and fish concentrations, and conservative exposure assumptions, the potential cancer risk was estimated for three scenarios: inhalation exposure to workers, inhalation exposure to residents on the mainland, and fish ingestion exposures to residents. U.S. EPA’s AERMOD model was used to estimate air concentrations in the immediate vicinity of the oil burns and NOAA’s HYSPLIT model was used to estimate more distant air concentrations and deposition rates. The lifetime incremental cancer risks were estimated as 6 x 10-8 for inhalation by workers, 6 x 10-12 for inhalation by onshore residents and 6 x 10-8 for fish consumption by residents.
    [Show full text]
  • The Societal Value of the HYSPLIT Air Dispersion Model
    The Societal Value of the HYSPLIT Air Dispersion Model Final Report to the NOAA Air Resources Laboratory Seth Villaneuva, Kathryne Cleary, and Alan Krupnick Report 21-04 February 2021 The Social Value of the HYSPLIT Model A About the Authors Seth Villanueva is a research analyst at Resources for the Future. He graduated from UC Santa Barbara in 2019 with a BA in economics and a minor in mathematics. At UCSB, Villanueva was an undergraduate Gretler Fellow research assistant studying the economic effects of scaled wind power generation. Villanueva’s work at RFF includes clean energy policy analysis, value of information research as part of the VALUABLES Consortium, and energy policy analysis for RFF’s annual Global Energy Outlook report. Kathryne Cleary is a senior research associate at Resources for the Future. Her work at RFF focuses primarily on electricity policy with the Future of Power Initiative and includes work on carbon pricing, electricity market design, and electrification. Outside of her work on electricity, Cleary has worked on economic studies on the value of information. Cleary holds an MEM with a focus on energy policy from the Yale School of the Environment and a BA in economics and environmental policy from Boston University. Alan Krupnick is a senior fellow at Resources for the Future. Krupnick’s research focuses on analyzing environmental and energy issues, in particular, the benefits, costs and design of pollution and energy policies, both in the United States and abroad. He leads RFF’s research on the risks, regulation and economics associated with shale gas development and has developed a portfolio of research on issues surrounding this newly plentiful fuel.
    [Show full text]
  • Inverse Modeling of Fire Emissions Constrained by Smoke Plume
    https://doi.org/10.5194/acp-2020-8 Preprint. Discussion started: 16 March 2020 c Author(s) 2020. CC BY 4.0 License. Inverse modeling of fire emissions constrained by smoke plume transport using HYSPLIT dispersion model and geostationary observations Hyun Cheol Kim 1,2, Tianfeng Chai1,2, Ariel Stein1 and Shobha Kondragunta3 1 Air Resources Laboratory, National Oceanic and Atmospheric Administration, College Park, MD, 20740, MD, USA 5 2 Cooperative Institute for Satellite Earth System Studies, University of Maryland, College Park, MD, 20740, USA 3 National Environmental Satellite, Data and Information Service, National Oceanic and Atmospheric Administration, College Park, MD 20740, USA Correspondence to: Tianfeng Chai ([email protected]), Hyun Cheol Kim ([email protected]) Abstract. Smoke forecasts have been challenged by high uncertainty in fire emission estimates. We develop an inverse 10 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 NOAA’s HYSPLIT and GOES Aerosol/Smoke Product (GASP), the system resolves smoke 15 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.
    [Show full text]
  • Long Range Transport of Southeast Asian PM2.5 Pollution to Northern Thailand During High Biomass Burning Episodes
    sustainability Article Long Range Transport of Southeast Asian PM2.5 Pollution to Northern Thailand during High Biomass Burning Episodes Teerachai Amnuaylojaroen 1,2,*, Jirarat Inkom 1, Radshadaporn Janta 3 and Vanisa Surapipith 3 1 School of Energy and Environment, University of Phayao, Phayao 56000, Thailand; [email protected] 2 Atmospheric Pollution and Climate Change Research Units, School of Energy and Environment, University of Phayao, Phayao 56000, Thailand 3 National Astronomical Research Institute of Thailand, Chiang Mai 53000, Thailand; [email protected] (R.J.); [email protected] (V.S.) * Correspondence: [email protected] or [email protected] Received: 14 October 2020; Accepted: 10 November 2020; Published: 2 December 2020 Abstract: This paper aims to investigate the potential contribution of biomass burning in PM2.5 pollution in Northern Thailand. We applied the coupled atmospheric and air pollution model which is based on the Weather Research and Forecasting Model (WRF) and a Hybrid Single-Particle Lagrangian Integrated Trajectory Model (HYSPLIT). The model output was compared to the ground-based measurements from the Pollution Control Department (PCD) to examine the model performance. As a result of the model evaluation, the meteorological variables agreed well with observations using the Index of Agreement (IOA) with ranges of 0.57 to 0.79 for temperature and 0.32 to 0.54 for wind speed, while the fractional biases of temperature and wind speed were 1.3 to 2.5 ◦C and 1.2 to 2.1 m/s. Analysis of the model and hotspots from the Moderate Imaging Spectroradiometer (MODIS) found that biomass burning from neighboring countries has greater potential to contribute to air pollution in northern Thailand than national emissions, which is indicated by the number of hotspot locations in Burma being greater than those in Thailand by two times under the influence of two major channels of Asian Monsoons, including easterly and northwesterly winds that bring pollutants from neighboring counties towards northern Thailand.
    [Show full text]
  • Wildland Fires and Air Pollution. Developments in Environmental
    Author's personal copy Developments in Environmental Science, Volume 8 499 A. Bytnerowicz, M. Arbaugh, A. Riebau and C. Andersen (Editors) Copyright r 2009 Elsevier B.V. All rights reserved. ISSN: 1474-8177/DOI:10.1016/S1474-8177(08)00022-3 Chapter 22 Regional Real-Time Smoke Prediction Systems Susan M. O’NeillÃ, Narasimhan (Sim) K. Larkin, Jeanne Hoadley, Graham Mills, Joseph K. Vaughan, Roland R. Draxler, Glenn Rolph, Mark Ruminski and Sue A. Ferguson Abstract Several real-time smoke prediction systems have been developed worldwide to help land managers, farmers, and air quality regulators balance land management needs against smoke impacts. Profiled here are four systems that are currently operational for regional domains for North America and Australia, providing forecasts to a well-developed user community. The systems link fire activity data, fuels information, and consumption and emissions models, with weather forecasts and dispersion models to produce a prediction of smoke concentrations from prescribed fires, wildfires, or agricultural fires across a region. The USDA Forest Service’s BlueSky system is operational for regional domains across the United States and obtains prescribed burn information and wildfire information from databases compiled by various agencies along with satellite fire detections. The U.S. National Oceanic and Atmospheric Adminis- tration (NOAA) smoke prediction system is initialized with satellite fire detections and is operational across North America. Washington State University’s ClearSky agricultural smoke prediction system is operational in the states of Idaho and Washington, and burn location information is input via a secure Web site by regulators in those states. The Australian Bureau of Meteorology smoke prediction system is operational for regional domains across Australia for wildfires and prescribed burning.
    [Show full text]
  • A Comparison of the Metum and CAMS Forecasts with Observations
    Models transport Saharan dust too low in the atmosphere: a comparison of the MetUM and CAMS forecasts with observations Debbie O’Sullivan1, Franco Marenco1, Claire L. Ryder2, Yaswant Pradhan1, Zak Kipling3, Ben Johnson1, Angela Benedetti3, Melissa Brooks1, Matthew McGill4, John Yorks4, Patrick Selmer4 5 1Met Office, Exeter, EX1 3PB, UK 2Department of Meteorology, University of Reading, RG6 6BB, UK 3European Centre for Medium-Range Weather Forecasts, Reading, RG2 9AX, UK 4NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA 10 Correspondence to: Franco Marenco ([email protected]) Abstract. We investigate the dust forecasts from two operational global atmospheric models in comparison with in-situ and remote sensing measurements obtained during the AERosol properties – Dust (AER-D) field campaign. Airborne elastic backscatter lidar measurements were performed on-board the Facility for Airborne Atmospheric Measurements during August 2015 over the Eastern Atlantic, and they permitted to characterize the dust vertical 15 distribution in detail, offering insights on transport from the Sahara. They were complemented with airborne in- situ measurements of dust size-distribution and optical properties, and datasets from the Cloud-Aerosol Transport System spaceborne lidar (CATS) and the Moderate Resolution Imaging Spectroradiometer (MODIS). We compare the airborne and spaceborne datasets to operational predictions obtained from the Met Office Unified Model (MetUM) and the Copernicus Atmosphere Monitoring Service (CAMS). The dust aerosol optical depth predictions 20 from the models are generally in agreement with the observations, but display a low bias. However, the predicted vertical distribution places the dust lower in the atmosphere than highlighted in our observations. This is particularly noticeable for the MetUM, which does not transport coarse dust high enough in the atmosphere, nor far enough away from source.
    [Show full text]
  • Refinery Emergency Air Monitoring Assessment Report
    Refinery Emergency Air Monitoring Assessment Report Objective 2: Evaluation of Air Monitoring Capabilities, Gaps, and Potential Enhancements March 2019 ARB Monitoring and Laboratory Division California Air Pollution Incident Air Monitoring Section Control Officers Association Table of Contents I. EXECUTIVE SUMMARY .................................................................................................................. 1 II. INTRODUCTION .............................................................................................................................. 12 III. BACKGROUND ................................................................................................................................ 12 IV. SCOPE OF THE REPORT ............................................................................................................. 17 V. EVALUATION OF AIR MONITORING CAPABILITIES, GAPS, AND POTENTIAL ENHANCEMENTS ........................................................................................................................... 23 A. MONITORING ............................................................................................................................... 24 1. Continuous Process Unit Monitoring ..................................................................................... 29 2. Refinery-Specific Ground Level Monitors ............................................................................. 32 3. Hand-Held and Portable Air Monitors – On-Site Use ........................................................
    [Show full text]