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Baker KR, Simon H, Henderson B, Tucker C, Cooley D, Zinsmeister E. Source-Receptor Relationships Between Precursor Emissions and O 3 and PM 2.5 Air Pollution Impacts. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:14626-14637. [PMID: 37721376 DOI: 10.1021/acs.est.3c03317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/19/2023]
Abstract
Reduced complexity tools that provide a representation of both primarily emitted particulate matter with an aerodynamic diameter less than 2.5 μm (PM2.5), secondarily formed PM2.5, and ozone (O3) allow for a quick assessment of many iterations of pollution control scenarios. Here, a new reduced complexity tool, Pattern Constructed Air Pollution Surfaces (PCAPS), that estimates annual average PM2.5 and seasonal average maximum daily average 8 h (MDA8) O3 for any source location in the United States is described and evaluated. Typically, reduced complexity tools are not evaluated for skill in predicting change in air pollution by comparison with more sophisticated modeling systems. Here, PCAPS was compared against multiple types of emission control scenarios predicted with state-of-the-science photochemical grid models to provide confidence that the model is realistically capturing the change in air pollution due to changing emissions. PCAPS was also applied with all anthropogenic emissions sources for multiple retrospective years to predict PM2.5 chemical components for comparison against routine surface measurements. PCAPS predicted similar magnitudes and regional variations in spatial gradients of measured chemical components of PM2.5. Model performance for capturing ambient measurements was consistent with other reduced complexity tools. PCAPS also did well at capturing the magnitude and spatial features of changes predicted by photochemical transport models for multiple emissions scenarios for both O3 and PM2.5. PCAPS is a flexible tool that provides source-receptor relationships using patterns of air quality gradients from a training data set of generic modeled sources to create interpolated air pollution gradients for new locations not part of the training database. The flexibility provided for both sources and receptors makes this tool ideal for integration into larger frameworks that provide emissions changes and need estimates of air quality to inform downstream analytics, which often includes an estimate of monetized health effects.
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Affiliation(s)
- Kirk R Baker
- U.S. Environmental Protection Agency, Research Triangle Park, North Carolina 27709, United States
| | - Heather Simon
- U.S. Environmental Protection Agency, Research Triangle Park, North Carolina 27709, United States
| | - Barron Henderson
- U.S. Environmental Protection Agency, Research Triangle Park, North Carolina 27709, United States
| | - Colby Tucker
- U.S. Environmental Protection Agency, Washington, D.C. 20460, United States
| | - David Cooley
- Abt Associates, Durham, North Carolina 27703, United States
| | - Emma Zinsmeister
- U.S. Environmental Protection Agency, Washington, D.C. 20460, United States
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2
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Koman PD, Billmire M, Baker KR, Carter JM, Thelen BJ, French NHF, Bell SA. Using wildland fire smoke modeling data in gerontological health research (California, 2007-2018). THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 838:156403. [PMID: 35660427 DOI: 10.1016/j.scitotenv.2022.156403] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 05/06/2022] [Accepted: 05/28/2022] [Indexed: 06/15/2023]
Abstract
Widespread population exposure to wildland fire smoke underscores the urgent need for new techniques to characterize fire-derived pollution for epidemiologic studies and to build climate-resilient communities especially for aging populations. Using atmospheric chemical transport modeling, we examined air quality with and without wildland fire smoke PM2.5. In 12-km gridded output, the 24-hour average concentration of all-source PM2.5 in California (2007-2018) was 5.16 μg/m3 (S.D. 4.66 μg/m3). The average concentration of fire-PM2.5 in California by year was 1.61 μg/m3 (~30% of total PM2.5). The contribution of fire-source PM2.5 ranged from 6.8% to 49%. We define a "smokewave" as two or more consecutive days with modeled levels above 35 μg/m3. Based on model-derived fire-PM2.5, 99.5% of California's population lived in a county that experienced at least one smokewave from 2007 to 2018, yet understanding of the impact of smoke on the health of aging populations is limited. Approximately 2.7 million (56%) of California residents aged 65+ years lived in counties representing the top 3 quartiles of fire-PM2.5 concentrations (2007-2018). For each year (2007-2018), grid cells containing skilled nursing facilities had significantly higher mean concentrations of all-source PM2.5 than cells without those facilities, but they also had generally lower mean concentrations of wildland fire-specific PM2.5. Compared to rural monitors in California, model predictions of wildland fire impacts on daily average PM2.5 carbon (organic and elemental) performed well most years but tended to overestimate wildland fire impacts for high-fire years. The modeling system isolated wildland fire PM2.5 from other sources at monitored and unmonitored locations, which is important for understanding exposures for aging population in health studies.
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Affiliation(s)
- Patricia D Koman
- University of Michigan, School of Public Health, Environmental Health Sciences, 1415 Washington Heights, Ann Arbor, MI 48109, USA.
| | - Michael Billmire
- Michigan Technological University, Michigan Tech Research Institute, 3600 Green Court, Suite 100, Ann Arbor, MI 48105, USA.
| | - Kirk R Baker
- U.S. Environmental Protection Agency, Office of Air and Radiation, Office of Air Quality Planning & Standards, Research Triangle Park, NC 27709, USA.
| | - Julie M Carter
- University of Michigan, School of Public Health, Environmental Health Sciences, 1415 Washington Heights, Ann Arbor, MI 48109, USA; Michigan Technological University, Michigan Tech Research Institute, 3600 Green Court, Suite 100, Ann Arbor, MI 48105, USA.
| | - Brian J Thelen
- Michigan Technological University, Michigan Tech Research Institute, 3600 Green Court, Suite 100, Ann Arbor, MI 48105, USA.
| | - Nancy H F French
- Michigan Technological University, Michigan Tech Research Institute, 3600 Green Court, Suite 100, Ann Arbor, MI 48105, USA.
| | - Sue Anne Bell
- University of Michigan, School of Nursing, Ann Arbor, MI 48109, USA.
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3
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Lien J, Hung HM. The contribution of transport and chemical processes on coastal ozone and emission control strategies to reduce ozone. Heliyon 2021; 7:e08210. [PMID: 34729439 PMCID: PMC8545683 DOI: 10.1016/j.heliyon.2021.e08210] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Revised: 09/13/2021] [Accepted: 10/15/2021] [Indexed: 11/25/2022] Open
Abstract
The interaction between transport and chemistry is pivotal for local ozone (O3) concentration, especially for a coastal region where the upstream sources might change diurnally. In the current emission control policy, most pollutants, such as particulate matter, SO2, NOx, and CO, decrease while the annual O3 trend might increase due to the complex feedbacks of precursors. In this study, we investigate the influence of transport upon the wintertime O3 diurnal trend over ZuoYing Kaohsiung, an urban coastal site in southern Taiwan, by constructing a two-dimensional numerical model coupling both physical mechanisms and core chemical processes and provide a feasible emission control strategy. The transport process (i.e., import vs. export) for the daytime is determined using the Leighton Ratio (Φ), the ratio of O3-production over O3-loss rate, under the pseudo-steady-state condition. Φ shows a deviation of -9 to +13% from the photo-stationary state, and experiences a transition from import effect before 10:15 to weakening import or net export effect afterward associated with a net O3 production as sea breeze starts developing. The significantly higher Φ derived from observation than from simulation by a factor of 1.35 might be resulted from the over-reported NO2 due to NOy contribution on the NO2 measurement, and the influence of aerosol and cloud possibly reducing ∼30% on applied NO2 photolysis rate constant, associated with aerosol optical depth of 0.75 ± 0.15 and single scattering albedo of 0.85 ± 0.15. In this studied NOx-saturated regime, the addition of sea breeze convergence over the land enhances the maximal O3 by ∼10%, mainly due to the O3 accumulation (∼88%). Furthermore, the ozone isopleth analysis as a function of non-methane hydrocarbons and NOx emissions provides an achievable strategy to decrease both maximum daily ozone and the increment of ozone from morning to maximum by reducing hydrocarbons and NOx emissions, which can also eliminate the additional nitrate contribution on the aerosols.
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Affiliation(s)
- Justin Lien
- Department of Atmospheric Sciences, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei, 10617, Taiwan
| | - Hui-Ming Hung
- Department of Atmospheric Sciences, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei, 10617, Taiwan
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4
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Hallar AG, Brown SS, Crosman E, Barsanti K, Cappa CD, Faloona I, Fast J, Holmes HA, Horel J, Lin J, Middlebrook A, Mitchell L, Murphy J, Womack CC, Aneja V, Baasandorj M, Bahreini R, Banta R, Bray C, Brewer A, Caulton D, de Gouw J, De Wekker SF, Farmer DK, Gaston CJ, Hoch S, Hopkins F, Karle NN, Kelly JT, Kelly K, Lareau N, Lu K, Mauldin RL, Mallia DV, Martin R, Mendoza D, Oldroyd HJ, Pichugina Y, Pratt KA, Saide P, Silva PJ, Simpson W, Stephens BB, Stutz J, Sullivan A. Coupled Air Quality and Boundary-Layer Meteorology in Western U.S. Basins during Winter: Design and Rationale for a Comprehensive Study. BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY 2021; 0:1-94. [PMID: 34446943 PMCID: PMC8384125 DOI: 10.1175/bams-d-20-0017.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Wintertime episodes of high aerosol concentrations occur frequently in urban and agricultural basins and valleys worldwide. These episodes often arise following development of persistent cold-air pools (PCAPs) that limit mixing and modify chemistry. While field campaigns targeting either basin meteorology or wintertime pollution chemistry have been conducted, coupling between interconnected chemical and meteorological processes remains an insufficiently studied research area. Gaps in understanding the coupled chemical-meteorological interactions that drive high pollution events make identification of the most effective air-basin specific emission control strategies challenging. To address this, a September 2019 workshop occurred with the goal of planning a future research campaign to investigate air quality in Western U.S. basins. Approximately 120 people participated, representing 50 institutions and 5 countries. Workshop participants outlined the rationale and design for a comprehensive wintertime study that would couple atmospheric chemistry and boundary-layer and complex-terrain meteorology within western U.S. basins. Participants concluded the study should focus on two regions with contrasting aerosol chemistry: three populated valleys within Utah (Salt Lake, Utah, and Cache Valleys) and the San Joaquin Valley in California. This paper describes the scientific rationale for a campaign that will acquire chemical and meteorological datasets using airborne platforms with extensive range, coupled to surface-based measurements focusing on sampling within the near-surface boundary layer, and transport and mixing processes within this layer, with high vertical resolution at a number of representative sites. No prior wintertime basin-focused campaign has provided the breadth of observations necessary to characterize the meteorological-chemical linkages outlined here, nor to validate complex processes within coupled atmosphere-chemistry models.
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Affiliation(s)
| | | | - Erik Crosman
- Department of Life, Earth, and Environmental Sciences, West Texas A&M University
| | - Kelley Barsanti
- Department of Chemical and Environmental Engineering, Center for Environmental Research and Technology, University of California, Riverside
| | - Christopher D. Cappa
- Department of Civil and Environmental Engineering, University of California, Davis 95616 USA
| | - Ian Faloona
- Department of Land, Air and Water Resources, University of California, Davis
| | - Jerome Fast
- Atmospheric Science and Global Change Division, Pacific Northwest, National Laboratory, Richland, Washington, USA
| | - Heather A. Holmes
- Department of Chemical Engineering, University of Utah, Salt Lake City, UT
| | - John Horel
- Department of Atmospheric Sciences, University of Utah, Salt Lake City, UT
| | - John Lin
- Department of Atmospheric Sciences, University of Utah, Salt Lake City, UT
| | | | - Logan Mitchell
- Department of Atmospheric Sciences, University of Utah, Salt Lake City, UT
| | - Jennifer Murphy
- Department of Chemistry, University of Toronto, Toronto, Ontario, Canada
| | - Caroline C. Womack
- Cooperative Institute for Research in Environmental Sciences, University of Colorado/ NOAA Chemical Sciences Laboratory, Boulder, CO
| | - Viney Aneja
- Department of Marine, Earth, and Atmospheric Sciences, North Carolina State University
| | | | - Roya Bahreini
- Environmental Sciences, University of California, Riverside, CA
| | | | - Casey Bray
- Department of Marine, Earth, and Atmospheric Sciences, North Carolina State University
| | - Alan Brewer
- NOAA Chemical Sciences Laboratory, Boulder, CO
| | - Dana Caulton
- Department of Atmospheric Science, University of Wyoming
| | - Joost de Gouw
- Cooperative Institute for Research in Environmental Sciences & Department of Chemistry, University of Colorado, Boulder, CO
| | | | | | - Cassandra J. Gaston
- Department of Atmospheric Science - Rosenstiel School of Marine and Atmospheric Science, University of Miami
| | - Sebastian Hoch
- Department of Atmospheric Sciences, University of Utah, Salt Lake City, UT
| | | | - Nakul N. Karle
- Environmental Science and Engineering, The University of Texas at El Paso, TX
| | - James T. Kelly
- Office of Air Quality Planning and Standards, US Environmental Protection Agency, Research Triangle Park, NC
| | - Kerry Kelly
- Chemical Engineering, University of Utah, Salt Lake City, UT
| | - Neil Lareau
- Atmospheric Sciences and Environmental Sciences and Health, University of Nevada, Reno, NV
| | - Keding Lu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Science and Engineering, Peking University, Beijing, China, 100871
| | - Roy L. Mauldin
- National Center for Atmospheric Research, Boulder, CO 80307, USA
| | - Derek V. Mallia
- Department of Atmospheric Sciences, University of Utah, Salt Lake City, UT
| | - Randal Martin
- Civil and Environmental Engineering, Utah State University, Utah Water Research Laboratory, Logan, UT
| | - Daniel Mendoza
- Department of Atmospheric Sciences, University of Utah, Salt Lake City, UT
| | - Holly J. Oldroyd
- Department of Civil and Environmental Engineering, University of California, Davis
| | | | | | - Pablo Saide
- Department of Atmospheric and Oceanic Sciences, and Institute of the Environment and Sustainability, University of California, Los Angeles
| | - Phillip J. Silva
- Food Animal Environmental Systems Research Unit, USDA-ARS, Bowling Green, KY
| | - William Simpson
- Department of Chemistry, Biochemistry, and Geophysical Institute, University of Alaska Fairbanks, Fairbanks, AK 99775-6160
| | - Britton B. Stephens
- Earth Observing Laboratory, National Center for Atmospheric Research, Boulder, CO
| | - Jochen Stutz
- Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles
| | - Amy Sullivan
- Department of Atmospheric Science, Colorado State University, Fort Collins, CO
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5
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Wang R, Guo X, Pan D, Kelly JT, Bash JO, Sun K, Paulot F, Clarisse L, Damme MV, Whitburn S, Coheur PF, Clerbaux C, Zondlo MA. Monthly Patterns of Ammonia Over the Contiguous United States at 2-km Resolution. GEOPHYSICAL RESEARCH LETTERS 2021; 48:10.1029/2020gl090579. [PMID: 34121780 PMCID: PMC8193802 DOI: 10.1029/2020gl090579] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2020] [Accepted: 12/06/2020] [Indexed: 06/12/2023]
Abstract
Monthly, high-resolution (∼2 km) ammonia (NH3) column maps from the Infrared Atmospheric Sounding Interferometer (IASI) were developed across the contiguous United States and adjacent areas. Ammonia hotspots (95th percentile of the column distribution) were highly localized with a characteristic length scale of 12 km and median area of 152 km2. Five seasonality clusters were identified with k-means++ clustering. The Midwest and eastern United States had a broad, spring maximum of NH3 (67% of hotspots in this cluster). The western United States, in contrast, showed a narrower midsummer peak (32% of hotspots). IASI spatiotemporal clustering was consistent with those from the Ammonia Monitoring Network. CMAQ and GFDL-AM3 modeled NH3 columns have some success replicating the seasonal patterns but did not capture the regional differences. The high spatial-resolution monthly NH3 maps serve as a constraint for model simulations and as a guide for the placement of future, ground-based network sites.
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Affiliation(s)
- Rui Wang
- Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ, USA
| | - Xuehui Guo
- Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ, USA
| | - Da Pan
- Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ, USA
| | - James T Kelly
- Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, RTP, NC, USA
| | - Jesse O Bash
- Office of Research and Development, U.S. Environmental Protection Agency, RTP, NC, USA
| | - Kang Sun
- Department of Civil, Structural and Environmental Engineering, University at Buffalo, Buffalo, NY, USA
| | - Fabien Paulot
- Geophysical Fluid Dynamics Laboratory, National Oceanic and Atmospheric Administration, Princeton, NJ, USA
| | - Lieven Clarisse
- Université Libre de Bruxelles (ULB), Spectroscopy, Quantum Chemistry and Atmospheric Remote Sensing (SQUARES), Brussels, Belgium
| | - Martin Van Damme
- Université Libre de Bruxelles (ULB), Spectroscopy, Quantum Chemistry and Atmospheric Remote Sensing (SQUARES), Brussels, Belgium
| | - Simon Whitburn
- Université Libre de Bruxelles (ULB), Spectroscopy, Quantum Chemistry and Atmospheric Remote Sensing (SQUARES), Brussels, Belgium
| | - Pierre-François Coheur
- Université Libre de Bruxelles (ULB), Spectroscopy, Quantum Chemistry and Atmospheric Remote Sensing (SQUARES), Brussels, Belgium
| | - Cathy Clerbaux
- Université Libre de Bruxelles (ULB), Spectroscopy, Quantum Chemistry and Atmospheric Remote Sensing (SQUARES), Brussels, Belgium
- LATMOS/IPSL, Sorbonne Université, UVSQ, CNRS, Paris, France
| | - Mark A Zondlo
- Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ, USA
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6
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Pye HOT, Nenes A, Alexander B, Ault AP, Barth MC, Clegg SL, Collett JL, Fahey KM, Hennigan CJ, Herrmann H, Kanakidou M, Kelly JT, Ku IT, McNeill VF, Riemer N, Schaefer T, Shi G, Tilgner A, Walker JT, Wang T, Weber R, Xing J, Zaveri RA, Zuend A. The Acidity of Atmospheric Particles and Clouds. ATMOSPHERIC CHEMISTRY AND PHYSICS 2020; 20:4809-4888. [PMID: 33424953 PMCID: PMC7791434 DOI: 10.5194/acp-20-4809-2020] [Citation(s) in RCA: 155] [Impact Index Per Article: 38.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
Acidity, defined as pH, is a central component of aqueous chemistry. In the atmosphere, the acidity of condensed phases (aerosol particles, cloud water, and fog droplets) governs the phase partitioning of semi-volatile gases such as HNO3, NH3, HCl, and organic acids and bases as well as chemical reaction rates. It has implications for the atmospheric lifetime of pollutants, deposition, and human health. Despite its fundamental role in atmospheric processes, only recently has this field seen a growth in the number of studies on particle acidity. Even with this growth, many fine particle pH estimates must be based on thermodynamic model calculations since no operational techniques exist for direct measurements. Current information indicates acidic fine particles are ubiquitous, but observationally-constrained pH estimates are limited in spatial and temporal coverage. Clouds and fogs are also generally acidic, but to a lesser degree than particles, and have a range of pH that is quite sensitive to anthropogenic emissions of sulfur and nitrogen oxides, as well as ambient ammonia. Historical measurements indicate that cloud and fog droplet pH has changed in recent decades in response to controls on anthropogenic emissions, while the limited trend data for aerosol particles indicates acidity may be relatively constant due to the semi-volatile nature of the key acids and bases and buffering in particles. This paper reviews and synthesizes the current state of knowledge on the acidity of atmospheric condensed phases, specifically particles and cloud droplets. It includes recommendations for estimating acidity and pH, standard nomenclature, a synthesis of current pH estimates based on observations, and new model calculations on the local and global scale.
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Affiliation(s)
- Havala O. T. Pye
- Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, 27711, USA
| | - Athanasios Nenes
- School of Architecture, Civil and Environmental Engineering, Ecole Polytechnique Fédérale de Lausanne, Lausanne, CH-1015, Switzerland
- Institute for Chemical Engineering Sciences, Foundation for Research and Technology Hellas, Patras, GR-26504, Greece
| | - Becky Alexander
- Department of Atmospheric Science, University of Washington, Seattle, WA, 98195, USA
| | - Andrew P. Ault
- Department of Chemistry, University of Michigan, Ann Arbor, MI, 48109-1055, USA
| | - Mary C. Barth
- National Center for Atmospheric Research, Boulder, CO, 80307, USA
| | - Simon L. Clegg
- School of Environmental Sciences, University of East Anglia, Norwich NR4 7TJ, UK
| | - Jeffrey L. Collett
- Department of Atmospheric Science, Colorado State University, Fort Collins, CO, 80523, USA
| | - Kathleen M. Fahey
- Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, 27711, USA
| | - Christopher J. Hennigan
- Department of Chemical, Biochemical, and Environmental Engineering, University of Maryland Baltimore County, Baltimore, MD, 21250, USA
| | - Hartmut Herrmann
- Leibniz Institute for Tropospheric Research (TROPOS), Atmospheric Chemistry Department (ACD), Leipzig, 04318, Germany
| | - Maria Kanakidou
- Department of Chemistry, University of Crete, Voutes, Heraklion Crete, 71003, Greece
| | - James T. Kelly
- Office of Air Quality Planning & Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC, 27711, USA
| | - I-Ting Ku
- Department of Atmospheric Science, Colorado State University, Fort Collins, CO, 80523, USA
| | - V. Faye McNeill
- Department of Chemical Engineering, Columbia University, New York, NY, 10027, USA
| | - Nicole Riemer
- Department of Atmospheric Sciences, University of Illinois at Urbana-Champaign, Urbana-Champaign, Illinois, 61801, USA
| | - Thomas Schaefer
- Leibniz Institute for Tropospheric Research (TROPOS), Atmospheric Chemistry Department (ACD), Leipzig, 04318, Germany
| | - Guoliang Shi
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, Nankai University, Tianjin, 300071, China
| | - Andreas Tilgner
- Leibniz Institute for Tropospheric Research (TROPOS), Atmospheric Chemistry Department (ACD), Leipzig, 04318, Germany
| | - John T. Walker
- Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, 27711, USA
| | - Tao Wang
- Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China
| | - Rodney Weber
- School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, GA, 30332, USA
| | - Jia Xing
- School of Environment, Tsinghua University, Beijing, 100084, China
| | - Rahul A. Zaveri
- Atmospheric Sciences & Global Change Division, Pacific Northwest National Laboratory, Richland, WA, 99352, USA
| | - Andreas Zuend
- Department of Atmospheric and Oceanic Sciences, McGill University, Montreal, Quebec, H3A 0B9, Canada
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7
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Jordan CE, Crawford JH, Beyersdorf AJ, Eck TF, Halliday HS, Nault BA, Chang LS, Park J, Park R, Lee G, Kim H, Ahn JY, Cho S, Shin HJ, Lee JH, Jung J, Kim DS, Lee M, Lee T, Whitehill A, Szykman J, Schueneman MK, Campuzano-Jost P, Jimenez JL, DiGangi JP, Diskin GS, Anderson BE, Moore RH, Ziemba LD, Fenn MA, Hair JW, Kuehn RE, Holz RE, Chen G, Travis K, Shook M, Peterson DA, Lamb KD, Schwarz JP. Investigation of factors controlling PM 2.5 variability across the South Korean Peninsula during KORUS-AQ. ELEMENTA (WASHINGTON, D.C.) 2020; 8:10.1525/elementa.424. [PMID: 33409323 PMCID: PMC7784633 DOI: 10.1525/elementa.424] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
The Korea - United States Air Quality Study (May - June 2016) deployed instrumented aircraft and ground-based measurements to elucidate causes of poor air quality related to high ozone and aerosol concentrations in South Korea. This work synthesizes data pertaining to aerosols (specifically, particulate matter with aerodynamic diameters <2.5 micrometers, PM2.5) and conditions leading to violations of South Korean air quality standards (24-hr mean PM2.5 < 35 μg m-3). PM2.5 variability from AirKorea monitors across South Korea is evaluated. Detailed data from the Seoul vicinity are used to interpret factors that contribute to elevated PM2.5. The interplay between meteorology and surface aerosols, contrasting synoptic-scale behavior vs. local influences, is presented. Transboundary transport from upwind sources, vertical mixing and containment of aerosols, and local production of secondary aerosols are discussed. Two meteorological periods are probed for drivers of elevated PM2.5. Clear, dry conditions, with limited transport (Stagnant period), promoted photochemical production of secondary organic aerosol from locally emitted precursors. Cloudy humid conditions fostered rapid heterogeneous secondary inorganic aerosol production from local and transported emissions (Transport/Haze period), likely driven by a positive feedback mechanism where water uptake by aerosols increased gas-to-particle partitioning that increased water uptake. Further, clouds reduced solar insolation, suppressing mixing, exacerbating PM2.5 accumulation in a shallow boundary layer. The combination of factors contributing to enhanced PM2.5 is challenging to model, complicating quantification of contributions to PM2.5 from local versus upwind precursors and production. We recommend co-locating additional continuous measurements at a few AirKorea sites across South Korea to help resolve this and other outstanding questions: carbon monoxide/carbon dioxide (transboundary transport tracer), boundary layer height (surface PM2.5 mixing depth), and aerosol composition with aerosol liquid water (meteorologically-dependent secondary production). These data would aid future research to refine emissions targets to further improve South Korean PM2.5 air quality.
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Affiliation(s)
- Carolyn E. Jordan
- National Institute of Aerospace, Hampton, Virginia, US
- NASA Langley Research Center, Hampton, Virginia, US
| | | | - Andreas J. Beyersdorf
- NASA Langley Research Center, Hampton, Virginia, US
- California State University, San Bernardino, California, US
| | - Thomas F. Eck
- NASA Goddard Space Flight Center, Greenbelt, Maryland, US
- Universities Space Research Association, Columbia, Maryland, US
| | - Hannah S. Halliday
- NASA Langley Research Center, Hampton, Virginia, US
- Universities Space Research Association, Columbia, Maryland, US
- EPA, Research Triangle Park, North Carolina, US
| | - Benjamin A. Nault
- Department of Chemistry, University of Colorado, Boulder, Colorado, US
- Cooperative Institute for Research in the Environmental Sciences, University of Colorado, Boulder, Colorado, US
| | - Lim-Seok Chang
- National Institute of Environmental Research, Air Quality Research Division, Incheon, KR
| | - JinSoo Park
- National Institute of Environmental Research, Air Quality Research Division, Incheon, KR
| | - Rokjin Park
- School of Earth and Environmental Sciences, Seoul National University, Seoul, KR
| | | | - Hwajin Kim
- Center for Environment, Health and Welfare Research, Korea Institute of Science and Technology, Seoul, KR
- Department of Energy and Environmental Engineering, University of Science and Technology, Daejeon, KR
| | - Jun-young Ahn
- National Institute of Environmental Research, Air Quality Research Division, Incheon, KR
| | - Seogju Cho
- Seoul Metropolitan Government Research Institute of Public Health and Environment, Gyeonggi-do, KR
| | - Hye Jung Shin
- National Institute of Environmental Research, Air Quality Research Division, Incheon, KR
| | | | - Jinsang Jung
- Center for Gas Analysis, Korea Research Institute of Standards and Science, Daejeon, KR
| | - Deug-Soo Kim
- Department of Environmental Engineering, Kunsan National University, Gunsan, KR
| | - Meehye Lee
- Department of Earth and Environmental Sciences, Korea University, Seoul, KR
| | | | - Andrew Whitehill
- US EPA/Office of Research and Development/Center for Environmental Measurement and Modeling, Research Triangle Park, North Carolina, US
| | - James Szykman
- NASA Langley Research Center, Hampton, Virginia, US
- US EPA/Office of Research and Development/Center for Environmental Measurement and Modeling, Research Triangle Park, North Carolina, US
| | - Melinda K. Schueneman
- Department of Chemistry, University of Colorado, Boulder, Colorado, US
- Cooperative Institute for Research in the Environmental Sciences, University of Colorado, Boulder, Colorado, US
| | - Pedro Campuzano-Jost
- Department of Chemistry, University of Colorado, Boulder, Colorado, US
- Cooperative Institute for Research in the Environmental Sciences, University of Colorado, Boulder, Colorado, US
| | - Jose L. Jimenez
- Department of Chemistry, University of Colorado, Boulder, Colorado, US
- Cooperative Institute for Research in the Environmental Sciences, University of Colorado, Boulder, Colorado, US
| | | | | | | | | | | | - Marta A. Fenn
- NASA Langley Research Center, Hampton, Virginia, US
- Science Systems and Applications Inc., Hampton, Virginia, US
| | | | - Ralph E. Kuehn
- Space Sciences Engineering Center, University of Wisconsin, Madison, Wisconsin, US
| | - Robert E. Holz
- Space Sciences Engineering Center, University of Wisconsin, Madison, Wisconsin, US
| | - Gao Chen
- NASA Langley Research Center, Hampton, Virginia, US
| | - Katherine Travis
- NASA Langley Research Center, Hampton, Virginia, US
- Universities Space Research Association, Columbia, Maryland, US
| | | | | | - Kara D. Lamb
- Cooperative Institute for Research in the Environmental Sciences, University of Colorado, Boulder, Colorado, US
- NOAA Earth System Research Laboratory, Chemical Sciences Division, Boulder, Colorado, US
| | - Joshua P. Schwarz
- NOAA Earth System Research Laboratory, Chemical Sciences Division, Boulder, Colorado, US
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8
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Di Q, Amini H, Shi L, Kloog I, Silvern R, Kelly J, Sabath MB, Choirat C, Koutrakis P, Lyapustin A, Wang Y, Mickley LJ, Schwartz J. An ensemble-based model of PM 2.5 concentration across the contiguous United States with high spatiotemporal resolution. ENVIRONMENT INTERNATIONAL 2019; 130:104909. [PMID: 31272018 PMCID: PMC7063579 DOI: 10.1016/j.envint.2019.104909] [Citation(s) in RCA: 268] [Impact Index Per Article: 53.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/07/2019] [Revised: 06/03/2019] [Accepted: 06/06/2019] [Indexed: 05/17/2023]
Abstract
Various approaches have been proposed to model PM2.5 in the recent decade, with satellite-derived aerosol optical depth, land-use variables, chemical transport model predictions, and several meteorological variables as major predictor variables. Our study used an ensemble model that integrated multiple machine learning algorithms and predictor variables to estimate daily PM2.5 at a resolution of 1 km × 1 km across the contiguous United States. We used a generalized additive model that accounted for geographic difference to combine PM2.5 estimates from neural network, random forest, and gradient boosting. The three machine learning algorithms were based on multiple predictor variables, including satellite data, meteorological variables, land-use variables, elevation, chemical transport model predictions, several reanalysis datasets, and others. The model training results from 2000 to 2015 indicated good model performance with a 10-fold cross-validated R2 of 0.86 for daily PM2.5 predictions. For annual PM2.5 estimates, the cross-validated R2 was 0.89. Our model demonstrated good performance up to 60 μg/m3. Using trained PM2.5 model and predictor variables, we predicted daily PM2.5 from 2000 to 2015 at every 1 km × 1 km grid cell in the contiguous United States. We also used localized land-use variables within 1 km × 1 km grids to downscale PM2.5 predictions to 100 m × 100 m grid cells. To characterize uncertainty, we used meteorological variables, land-use variables, and elevation to model the monthly standard deviation of the difference between daily monitored and predicted PM2.5 for every 1 km × 1 km grid cell. This PM2.5 prediction dataset, including the downscaled and uncertainty predictions, allows epidemiologists to accurately estimate the adverse health effect of PM2.5. Compared with model performance of individual base learners, an ensemble model would achieve a better overall estimation. It is worth exploring other ensemble model formats to synthesize estimations from different models or from different groups to improve overall performance.
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Affiliation(s)
- Qian Di
- Department of Environmental Health, Harvard T.H. Chan School of Public Heath, Boston, MA, United States; Research Center for Public Health, Tsinghua University, Beijing, China.
| | - Heresh Amini
- Department of Environmental Health, Harvard T.H. Chan School of Public Heath, Boston, MA, United States
| | - Liuhua Shi
- Department of Environmental Health, Harvard T.H. Chan School of Public Heath, Boston, MA, United States
| | - Itai Kloog
- Department of Geography and Environmental Development, Ben-Gurion University of the Negev, Beer Sheva, Israel
| | - Rachel Silvern
- Department of Earth and Planetary Sciences, Harvard University, Cambridge, MA, United States
| | - James Kelly
- U.S. Environmental Protection Agency, Office of Air Quality Planning & Standards, Research Triangle Park, NC, United States
| | - M Benjamin Sabath
- Department of Biostatistics, Harvard T.H. Chan School of Public Heath, Boston, MA, United States
| | - Christine Choirat
- Department of Biostatistics, Harvard T.H. Chan School of Public Heath, Boston, MA, United States
| | - Petros Koutrakis
- Department of Environmental Health, Harvard T.H. Chan School of Public Heath, Boston, MA, United States
| | | | - Yujie Wang
- University of Maryland, Baltimore County, Baltimore, MD, United States
| | - Loretta J Mickley
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, United States
| | - Joel Schwartz
- Department of Environmental Health, Harvard T.H. Chan School of Public Heath, Boston, MA, United States
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9
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Barrera YD, Nehrkorn T, Hegarty J, Sargent M, Benmergui J, Gottlieb E, Wofsy SC, DeCola P, Hutyra L, Jones T. Using Lidar Technology To Assess Urban Air Pollution and Improve Estimates of Greenhouse Gas Emissions in Boston. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2019; 53:8957-8966. [PMID: 31265266 DOI: 10.1021/acs.est.9b00650] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Simulation of the planetary boundary layer (PBL) is key for forecasting air quality and estimating greenhouse gas (GHG) emissions in cities. Here we conducted the first long-term and continuous study of PBL heights (PBLHs) in Boston, MA, using a compact lidar instrument. We developed an image recognition algorithm to estimate PBLHs from the lidar measurements and evaluated simulations of the PBL from seven numerical weather prediction (NWP) model versions, which showed different systematic errors and variability in simulating the PBLHs (discrepancies from -2.5 to 4.0 km). The NWP model with the best overall agreement for the fully developed PBL had R2 = 0.72 and a bias of only 0.128 km. However, this model predicted a notable number of anomalously high carbon dioxide concentrations at ground stations, because it occasionally significantly underestimated the PBLH. We also developed a novel method that combines lidar data with footprints from a Lagrangian particle dispersion model to identify long-range transport of air pollution in the nocturnal residual layer. Our framework was powerful in evaluating the performance of models used to estimate air pollution and GHG emissions in cities, which is critical to track progress on emission reduction targets and guide effective policies.
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Affiliation(s)
- Yanina D Barrera
- School of Engineering and Applied Sciences and Department of Earth and Planetary Sciences , Harvard University , Cambridge , Massachusetts 02138 , United States
| | - Thomas Nehrkorn
- Atmospheric and Environmental Research, Inc. , Lexington , Massachusetts 02421 , United States
| | - Jennifer Hegarty
- Atmospheric and Environmental Research, Inc. , Lexington , Massachusetts 02421 , United States
| | - Maryann Sargent
- School of Engineering and Applied Sciences and Department of Earth and Planetary Sciences , Harvard University , Cambridge , Massachusetts 02138 , United States
| | - Joshua Benmergui
- School of Engineering and Applied Sciences and Department of Earth and Planetary Sciences , Harvard University , Cambridge , Massachusetts 02138 , United States
| | - Elaine Gottlieb
- School of Engineering and Applied Sciences and Department of Earth and Planetary Sciences , Harvard University , Cambridge , Massachusetts 02138 , United States
| | - Steven C Wofsy
- School of Engineering and Applied Sciences and Department of Earth and Planetary Sciences , Harvard University , Cambridge , Massachusetts 02138 , United States
| | - Phil DeCola
- Sigma Space Corporation , Lanham , Maryland 20706 , United States
- Department of Atmospheric and Oceanic Sciences , University of Maryland , College Park , Maryland 20742 , United States
| | - Lucy Hutyra
- Department of Earth and Environment , Boston University , Boston , Massachusetts 02215 , United States
| | - Taylor Jones
- School of Engineering and Applied Sciences and Department of Earth and Planetary Sciences , Harvard University , Cambridge , Massachusetts 02138 , United States
- Sigma Space Corporation , Lanham , Maryland 20706 , United States
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10
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Koman PD, Billmire M, Baker KR, de Majo R, Anderson FJ, Hoshiko S, Thelen BJ, French NH. Mapping Modeled Exposure of Wildland Fire Smoke for Human Health Studies in California. ATMOSPHERE 2019; 10:308. [PMID: 31803514 PMCID: PMC6892473 DOI: 10.3390/atmos10060308] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
Wildland fire smoke exposure affects a broad proportion of the U.S. population and is increasing due to climate change, settlement patterns and fire seclusion. Significant public health questions surrounding its effects remain, including the impact on cardiovascular disease and maternal health. Using atmospheric chemical transport modeling, we examined general air quality with and without wildland fire smoke PM2.5. The 24-h average concentration of PM2.5 from all sources in 12-km gridded output from all sources in California (2007-2013) was 4.91 μg/m3. The average concentration of fire-PM2.5 in California by year was 1.22 μg/m3 (~25% of total PM2.5). The fire-PM2.5 daily mean was estimated at 4.40 μg/m3 in a high fire year (2008). Based on the model-derived fire-PM2.5 data, 97.4% of California's population lived in a county that experienced at least one episode of high smoke exposure ("smokewave") from 2007-2013. Photochemical model predictions of wildfire impacts on daily average PM2.5 carbon (organic and elemental) compared to rural monitors in California compared well for most years but tended to over-estimate wildfire impacts for 2008 (2.0 μg/m3 bias) and 2013 (1.6 μg/m3 bias) while underestimating for 2009 (-2.1 μg/m3 bias). The modeling system isolated wildfire and PM2.5 from other sources at monitored and unmonitored locations, which is important for understanding population exposure in health studies. Further work is needed to refine model predictions of wildland fire impacts on air quality in order to increase confidence in the model for future assessments. Atmospheric modeling can be a useful tool to assess broad geographic scale exposure for epidemiologic studies and to examine scenario-based health impacts.
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Affiliation(s)
- Patricia D. Koman
- Environmental Health Sciences, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA
| | - Michael Billmire
- Michigan Tech Research Institute, Michigan Technological University, Ann Arbor, MI, 48105 USA
| | - Kirk R. Baker
- Office of Air Quality Planning & Standards, Office of Air and Radiation, U.S. Environmental Protection Agency, Research Triangle Park, NC, 27709 USA
| | - Ricardo de Majo
- Health Behavior Health Education, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
| | - Frank J. Anderson
- Obstetrics and Gynecology, University of Michigan School of Medicine, Ann Arbor, MI 48109, USA
| | - Sumi Hoshiko
- Environmental Health Investigations Branch, California Department of Public Health, Richmond, CA 94804,USA
| | - Brian J. Thelen
- Michigan Tech Research Institute, Michigan Technological University, Ann Arbor, MI, 48105 USA
| | - Nancy H.F. French
- Michigan Tech Research Institute, Michigan Technological University, Ann Arbor, MI, 48105 USA
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11
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Kelly JT, Koplitz SN, Baker KR, Holder AL, Pye HOT, Murphy BN, Bash JO, Henderson BH, Possiel N, Simon H, Eyth AM, Jang C, Phillips S, Timin B. Assessing PM 2.5 Model Performance for the Conterminous U.S. with Comparison to Model Performance Statistics from 2007-2015. ATMOSPHERIC ENVIRONMENT (OXFORD, ENGLAND : 1994) 2019; 214:1-116872. [PMID: 31741655 PMCID: PMC6859642 DOI: 10.1016/j.atmosenv.2019.116872] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Previous studies have proposed that model performance statistics from earlier photochemical grid model (PGM) applications can be used to benchmark performance in new PGM applications. A challenge in implementing this approach is that limited information is available on consistently calculated model performance statistics that vary spatially and temporally over the U.S. Here, a consistent set of model performance statistics are calculated by year, season, region, and monitoring network for PM2.5 and its major components using simulations from versions 4.7.1-5.2.1 of the Community Multiscale Air Quality (CMAQ) model for years 2007-2015. The multi-year set of statistics is then used to provide quantitative context for model performance results from the 2015 simulation. Model performance for PM2.5 organic carbon in the 2015 simulation ranked high (i.e., favorable performance) in the multi-year dataset, due to factors including recent improvements in biogenic secondary organic aerosol and atmospheric mixing parameterizations in CMAQ. Model performance statistics for the Northwest region in 2015 ranked low (i.e., unfavorable performance) for many species in comparison to the 2007-2015 dataset. This finding motivated additional investigation that suggests a need for improved speciation of wildfire PM2.5emissions and modeling of boundary layer dynamics near water bodies. Several limitations were identified in the approach of benchmarking new model performance results with previous results. Since performance statistics vary widely by region and season, a simple set of national performance benchmarks (e.g., one or two targets per species and statistic) as proposed previously are inadequate to assess model performance throughout the U.S. Also, trends in model performance statistics for sulfate over the 2007 to 2015 period suggest that model performance for earlier years may not be a useful reference for assessing model performance for recent years in some cases. Comparisons of results from the 2015 base case with results from five sensitivity simulations demonstrated the importance of parameterizations of NH3 surface exchange, organic aerosol volatility and production, and emissions of crustal cations for predicting PM2.5 species concentrations.
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Affiliation(s)
- James T Kelly
- Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Shannon N Koplitz
- Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Kirk R Baker
- Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Amara L Holder
- Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Havala O T Pye
- Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Benjamin N Murphy
- Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Jesse O Bash
- Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Barron H Henderson
- Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Norm Possiel
- Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Heather Simon
- Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Alison M Eyth
- Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Carey Jang
- Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Sharon Phillips
- Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Brian Timin
- Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
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