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Baker KR, Valin L, Szykman J, Judd L, Shu Q, Hutzell B, Napelenok S, Murphy B, Connors V. Photochemical model assessment of single source NO 2 and O 3 plumes using field study data. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 903:166606. [PMID: 37640074 DOI: 10.1016/j.scitotenv.2023.166606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 08/23/2023] [Accepted: 08/25/2023] [Indexed: 08/31/2023]
Abstract
Single source contribution to ambient O3 and PM2.5 has been estimated with photochemical grid models to support policy demonstrations for National Ambient Air Quality Standards, regional haze, and permit related programs. Limited field data exists to evaluate model representation of the spatial extent and chemical composition of plumes emitted by specific facilities. New tropospheric column measurements of NO2 and in-plume chemical measurements downwind of specific facilities allows for photochemical model evaluation of downwind plume extent, grid resolution impacts on plume concentration gradients, and source attribution methods. Here, photochemical models were applied with source sensitivity and source apportionment approaches to differentiate single source impacts on NO2 and O3 and compare with field study measurements. Source sensitivity approaches (e.g., brute-force difference method and decoupled direct method (DDM)) captured the spatial extent of NO2 plumes downwind of three facilities and the transition of near-source O3 titration to downwind production. Source apportionment approaches showed variability in terms of attributing the spatial extent of NO2 plumes and downwind O3 production. Each of the Community Multiscale Air Quality (CMAQ) source apportionment options predicted large O3 contribution from a large industrial facility in the flight transects nearest the facility when measurements and source sensitivity approaches suggest titration was outpacing production. In general, CMAQ DDM tends to attribute more O3 to boundary inflow and less to within-domain NOX and VOC sources compared to CMAQ source apportionment. The photochemical modeling system was able to capture single source plumes using 1 to 12 km grid resolution with best representation of plume extent and magnitude at the finer resolutions. When modeled at 1 to 12 km grid resolution, primary and secondary PM2.5 impacts were highest at the source location and decrease as distance increases downwind. The use of coarser grid resolution for single source attribution resulted in predicted impacts highest near the source but lower peak source specific concentrations compared to finer grid resolution simulations because impacts were spread out over a larger area.
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Affiliation(s)
- Kirk R Baker
- U.S. Environmental Protection Agency, Research Triangle Park, NC, USA.
| | - Lukas Valin
- U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Jim Szykman
- U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Laura Judd
- NASA Langley Research Center, Hampton, VA, USA
| | - Qian Shu
- U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Bill Hutzell
- U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Sergey Napelenok
- U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Ben Murphy
- U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
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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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Simon H, Baker KR, Sellers J, Amend M, Penn SL, Bankert J, Chan EAW, Fann N, Jang C, McKinley G, Zawacki M, Roman H. Evaluating reduced-form modeling tools for simulating ozone and PM 2.5 monetized health impacts. ENVIRONMENTAL SCIENCE: ATMOSPHERES 2023; 19:1-13. [PMID: 37590244 PMCID: PMC10425884 DOI: 10.1039/d3ea00092c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/19/2023]
Abstract
Reduced-form modeling approaches are an increasingly popular way to rapidly estimate air quality and human health impacts related to changes in air pollutant emissions. These approaches reduce computation time by making simplifying assumptions about pollutant source characteristics, transport and chemistry. Two reduced form tools used by the Environmental Protection Agency in recent assessments are source apportionment-based benefit per ton (SA BPT) and source apportionment-based air quality surfaces (SABAQS). In this work, we apply these two reduced form tools to predict changes in ambient summer-season ozone, ambient annual PM2.5 component species and monetized health benefits for multiple sector-specific emission control scenarios: on-road mobile, electricity generating units (EGUs), cement kilns, petroleum refineries, and pulp and paper facilities. We then compare results against photochemical grid and standard health model-based estimates. We additionally compare monetized PM2.5 health benefits to values derived from three reduced form tools available in the literature: the Intervention Model for Air Pollution (InMAP), Air Pollution Emission Experiments and Policy Analysis (APEEP) version 2 (AP2) and Estimating Air pollution Social Impact Using Regression (EASIUR). Ozone and PM2.5 changes derived from SABAQS for EGU scenarios were well-correlated with values obtained from photochemical modeling simulations with spatial correlation coefficients between 0.64 and 0.89 for ozone and between 0.75 and 0.94 for PM2.5. SABAQS ambient ozone and PM2.5 bias when compared to photochemical modeling predictions varied by emissions scenario: SABAQS PM2.5 changes were overpredicted by up to 46% in one scenario and underpredicted by up to 19% in another scenario; SABAQS seasonal ozone changes were overpredicted by 34% to 83%. All tools predicted total PM2.5 benefits within a factor of 2 of the full-form predictions consistent with intercomparisons of reduced form tools available in the literature. As reduced form tools evolve, it is important to continue periodic comparison with comprehensive models to identify systematic biases in estimating air pollution impacts and resulting monetized health benefits.
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Affiliation(s)
- Heather Simon
- US Environmental Protection Agency, Office of Air Quality Planning and Standards, Research Triangle Park, NC
| | - Kirk R Baker
- US Environmental Protection Agency, Office of Air Quality Planning and Standards, Research Triangle Park, NC
| | - Jennifer Sellers
- US Environmental Protection Agency, Office of Air Quality Planning and Standards, Research Triangle Park, NC
| | | | | | | | - Elizabeth A W Chan
- US Environmental Protection Agency, Office of Air Quality Planning and Standards, Research Triangle Park, NC
| | - Neal Fann
- US Environmental Protection Agency, Office of Air Quality Planning and Standards, Research Triangle Park, NC
| | - Carey Jang
- US Environmental Protection Agency, Office of Air Quality Planning and Standards, Research Triangle Park, NC
| | - Gobeail McKinley
- US Environmental Protection Agency, Office of Air Quality Planning and Standards, Research Triangle Park, NC
| | - Margaret Zawacki
- US Environmental Protection Agency, Office of Transportation and Air Quality, Ann Arbor, MI
| | - Henry Roman
- Industrial Economics, Incorporated, Cambridge, MA
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Hogrefe C, Bash JO, Pleim JE, Schwede DB, Gilliam RC, Foley KM, Appel KW, Mathur R. An Analysis of CMAQ Gas Phase Dry Deposition over North America Through Grid-Scale and Land-Use Specific Diagnostics in the Context of AQMEII4. ATMOSPHERIC CHEMISTRY AND PHYSICS 2023; 23:8119-8147. [PMID: 37942278 PMCID: PMC10631556 DOI: 10.5194/acp-23-8119-2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2023]
Abstract
The fourth phase of the Air Quality Model Evaluation International Initiative (AQMEII4) is conducting a diagnostic intercomparison and evaluation of deposition simulated by regional-scale air quality models over North America and Europe. In this study, we analyze annual AQMEII4 simulations performed with the Community Multiscale Air Quality Model (CMAQ) version 5.3.1 over North America. These simulations were configured with both the M3Dry and Surface Tiled Aerosol and Gas Exchange (STAGE) dry deposition schemes available in CMAQ. A comparison of observed and modeled concentrations and wet deposition fluxes shows that the AQMEII4 CMAQ simulations perform similarly to other contemporary regional-scale modeling studies. During summer, M3Dry has higher ozone (O3) deposition velocities (Vd) and lower mixing ratios than STAGE for much of the eastern U.S. while the reverse is the case over eastern Canada and along the West Coast. In contrast, during winter STAGE has higher O3 Vd and lower mixing ratios than M3Dry over most of the southern half of the modeling domain while the reverse is the case for much of the northern U.S. and southern Canada. Analysis of the diagnostic variables defined for the AQMEII4 project, i.e. grid-scale and land-use (LU) specific effective conductances and deposition fluxes for the major dry deposition pathways, reveals generally higher summertime stomatal and wintertime cuticular grid-scale effective conductances for M3Dry and generally higher soil grid-scale effective conductances (for both vegetated and bare soil) for STAGE in both summer and winter. On a domain-wide basis, the stomatal grid-scale effective conductances account for about half of the total O3 Vd during daytime hours in summer for both schemes. Employing LU-specific diagnostics, results show that daytime Vd varies by a factor of 2 between LU categories. Furthermore, M3Dry vs. STAGE differences are most pronounced for the stomatal and vegetated soil pathway for the forest LU categories, with M3Dry estimating larger effective conductances for the stomatal pathway and STAGE estimating larger effective conductances for the vegetated soil pathway for these LU categories. Annual domain total O3 deposition fluxes differ only slightly between M3Dry (74.4 Tg/year) and STAGE (76.2 Tg/yr), but pathway-specific fluxes to individual LU types can vary more substantially on both annual and seasonal scales which would affect estimates of O3 damages to sensitive vegetation. A comparison of two simulations differing only in their LU classification scheme shows that the differences in LU cause seasonal mean O3 mixing ratio differences on the order of 1 ppb across large portions of the domain, with the differences generally largest during summer and in areas characterized by the largest differences in the fractional coverages of the forest, planted/cultivated, and grassland LU categories. These differences are generally smaller than the M3Dry vs. STAGE differences outside the summer season but have a similar magnitude during summer. Results indicate that the deposition impacts of LU differences are caused both by differences in the fractional coverages and spatial distributions of different LU categories as well as the characterization of these categories through variables like surface roughness and vegetation fraction in look-up tables used in the land-surface model and deposition schemes. Overall, the analyses and results presented in this study illustrate how the diagnostic grid-scale and LU-specific dry deposition variables adopted for AQMEII4 can provide insights into similarities and differences between the CMAQ M3Dry and STAGE dry deposition schemes that affect simulated pollutant budgets and ecosystem impacts from atmospheric pollution.
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Affiliation(s)
- Christian Hogrefe
- Center for Environmental Measurement and Modeling, US Environmental Protection Agency, 109 T.W. Alexander Dr., P.O. Box 12055, RTP, NC 27711, USA
| | - Jesse O Bash
- Center for Environmental Measurement and Modeling, US Environmental Protection Agency, 109 T.W. Alexander Dr., P.O. Box 12055, RTP, NC 27711, USA
| | - Jonathan E Pleim
- Center for Environmental Measurement and Modeling, US Environmental Protection Agency, 109 T.W. Alexander Dr., P.O. Box 12055, RTP, NC 27711, USA
| | - Donna B Schwede
- Center for Environmental Measurement and Modeling, US Environmental Protection Agency, 109 T.W. Alexander Dr., P.O. Box 12055, RTP, NC 27711, USA
| | - Robert C Gilliam
- Center for Environmental Measurement and Modeling, US Environmental Protection Agency, 109 T.W. Alexander Dr., P.O. Box 12055, RTP, NC 27711, USA
| | - Kristen M Foley
- Center for Environmental Measurement and Modeling, US Environmental Protection Agency, 109 T.W. Alexander Dr., P.O. Box 12055, RTP, NC 27711, USA
| | - K Wyat Appel
- Center for Environmental Measurement and Modeling, US Environmental Protection Agency, 109 T.W. Alexander Dr., P.O. Box 12055, RTP, NC 27711, USA
| | - Rohit Mathur
- Center for Environmental Measurement and Modeling, US Environmental Protection Agency, 109 T.W. Alexander Dr., P.O. Box 12055, RTP, NC 27711, USA
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Sarwar G, Hogrefe C, Henderson BH, Foley K, Mathur R, Murphy B, Ahmed S. Characterizing variations in ambient PM 2.5 concentrations at the U.S. Embassy in Dhaka, Bangladesh using observations and the CMAQ modeling system. ATMOSPHERIC ENVIRONMENT (OXFORD, ENGLAND : 1994) 2023; 296:119587. [PMID: 37854171 PMCID: PMC10581604 DOI: 10.1016/j.atmosenv.2023.119587] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/20/2023]
Abstract
We analyze hourly PM2.5 (particles with an aerodynamic diameter of ≤ 2.5 μm) concentrations measured at the U.S. Embassy in Dhaka over the 2016 - 2021 time period and find that concentrations are seasonally dependent with the highest occurring in winter and the lowest in monsoon seasons. Mean winter PM2.5 concentrations reached ~165-175 μg/m3 while monsoon concentrations remained ~30-35 μg/m3. Annual mean PM2.5 concentration reached ~5-6 times greater than the Bangladesh annual PM2.5 standard of 15 μg/m3. The number of days exceeding the daily PM2.5 standard of 65 μg/m3 in a year approached nearly 50%. Daily-mean PM2.5 concentrations remained elevated (>65 μg/m3) for more than 80 consecutive days. Night-time concentrations were greater than daytime concentrations. The comparison of results obtained from the Community Multiscale Air Quality (CMAQ) model simulations over the Northern Hemisphere using 108-km horizontal grids with observed data suggests that the model can reproduce the seasonal variation of observed data but underpredicts observed PM2.5 in winter months with a normalized mean bias of 13-32%. In the model, organic aerosol is the largest component of PM2.5, of which secondary organic aerosol plays a dominant role. Transboundary pollution has a large impact on the PM2.5 concentration in Dhaka, with an annual mean contribution of ~40 μg/m3.
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Affiliation(s)
- Golam Sarwar
- Center for Environmental Measurement & Modeling, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Christian Hogrefe
- Center for Environmental Measurement & Modeling, 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
| | - Kristen Foley
- Center for Environmental Measurement & Modeling, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Rohit Mathur
- Center for Environmental Measurement & Modeling, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Ben Murphy
- Center for Environmental Measurement & Modeling, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Shoeb Ahmed
- Department of Chemical Engineering, Bangladesh University of Engineering and Technology, Dhaka-1000, Bangladesh
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6
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Seltzer KM, Murphy BN, Pennington EA, Allen C, Talgo K, Pye HOT. Volatile Chemical Product Enhancements to Criteria Pollutants in the United States. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2022; 56:6905-6913. [PMID: 34779612 PMCID: PMC9247718 DOI: 10.1021/acs.est.1c04298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Volatile chemical products (VCPs) are a significant source of reactive organic carbon emissions in the United States with a substantial fraction (>20% by mass) serving as secondary organic aerosol (SOA) precursors. Here, we incorporate a new nationwide VCP inventory into the Community Multiscale Air Quality (CMAQ) model with VCP-specific updates to better model air quality impacts. Model results indicate that VCPs mostly enhance anthropogenic SOA in densely populated areas with population-weighted annual average SOA increasing 15-30% in Southern California and New York City due to VCP emissions (contribution of 0.2-0.5 μg m-3). Annually, VCP emissions enhance total population-weighted PM2.5 by ∼5% in California, ∼3% in New York, New Jersey, and Connecticut, and 1-2% in most other states. While the maximum daily 8 h ozone enhancements from VCP emissions are more modest, their influence can cause a several ppb increase on select days in major cities. Printing Inks, Cleaning Products, and Paints and Coatings product use categories contribute ∼75% to the modeled VCP-derived SOA and Cleaning Products, Paints and Coatings, and Personal Care Products contribute ∼81% to the modeled VCP-derived ozone. Overall, VCPs enhance multiple criteria pollutants throughout the United States with the largest impacts in urban cores.
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Affiliation(s)
- Karl M. Seltzer
- Oak Ridge Institute for Science and Education Postdoctoral Fellow in the Office of Research and Development, U.S. Environmental Protection Agency, 109 TW Alexander Dr, Research Triangle Park, NC 27711
| | - Benjamin N. Murphy
- Office of Research and Development, U.S. Environmental Protection Agency, 109 TW Alexander Dr, Research Triangle Park, NC 27711
| | - Elyse A. Pennington
- Department of Chemical Engineering, California Institute of Technology, Pasadena, CA 91125
| | - Chris Allen
- General Dynamics Information Technology, Research Triangle Park, NC, 27711
| | - Kevin Talgo
- General Dynamics Information Technology, Research Triangle Park, NC, 27711
| | - Havala O. T. Pye
- Office of Research and Development, U.S. Environmental Protection Agency, 109 TW Alexander Dr, Research Triangle Park, NC 27711
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Kenagy HS, Romer Present PS, Wooldridge PJ, Nault BA, Campuzano-Jost P, Day DA, Jimenez JL, Zare A, Pye HOT, Yu J, Song CH, Blake DR, Woo JH, Kim Y, Cohen RC. Contribution of Organic Nitrates to Organic Aerosol over South Korea during KORUS-AQ. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2021; 55:16326-16338. [PMID: 34870986 PMCID: PMC8759034 DOI: 10.1021/acs.est.1c05521] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
The role of anthropogenic NOx emissions in secondary organic aerosol (SOA) production is not fully understood but is important for understanding the contribution of emissions to air quality. Here, we examine the role of organic nitrates (RONO2) in SOA formation over the Korean Peninsula during the Korea-United States Air Quality field study in Spring 2016 as a model for RONO2 aerosol in cities worldwide. We use aircraft-based measurements of the particle phase and total (gas + particle) RONO2 to explore RONO2 phase partitioning. These measurements show that, on average, one-fourth of RONO2 are in the condensed phase, and we estimate that ≈15% of the organic aerosol (OA) mass can be attributed to RONO2. Furthermore, we observe that the fraction of RONO2 in the condensed phase increases with OA concentration, evidencing that equilibrium absorptive partitioning controls the RONO2 phase distribution. Lastly, we model RONO2 chemistry and phase partitioning in the Community Multiscale Air Quality modeling system. We find that known chemistry can account for one-third of the observed RONO2, but there is a large missing source of semivolatile, anthropogenically derived RONO2. We propose that this missing source may result from the oxidation of semi- and intermediate-volatility organic compounds and/or from anthropogenic molecules that undergo autoxidation or multiple generations of OH-initiated oxidation.
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Affiliation(s)
- Hannah S Kenagy
- Department of Chemistry, University of California, Berkeley, California 94720, United States
| | - Paul S Romer Present
- Department of Chemistry, University of California, Berkeley, California 94720, United States
| | - Paul J Wooldridge
- Department of Chemistry, University of California, Berkeley, California 94720, United States
| | - Benjamin A Nault
- Department of Chemistry, University of Colorado, Boulder, Colorado 80309, United States
- Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, Colorado 80309, United States
| | - Pedro Campuzano-Jost
- Department of Chemistry, University of Colorado, Boulder, Colorado 80309, United States
- Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, Colorado 80309, United States
| | - Douglas A Day
- Department of Chemistry, University of Colorado, Boulder, Colorado 80309, United States
- Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, Colorado 80309, United States
| | - Jose L Jimenez
- Department of Chemistry, University of Colorado, Boulder, Colorado 80309, United States
- Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, Colorado 80309, United States
| | - Azimeh Zare
- Department of Chemistry, University of California, Berkeley, California 94710, United States
| | - Havala O T Pye
- Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, Durham, North Carolina 27709, United States
| | - Jinhyeok Yu
- School of Earth Sciences and Environmental Engineering, Gwangju Institute of Science and Technology (GIST), Gwangju 61105, Republic of Korea
| | - Chul H Song
- School of Earth Sciences and Environmental Engineering, Gwangju Institute of Science and Technology (GIST), Gwangju 61105, Republic of Korea
| | - Donald R Blake
- Department of Chemistry, University of California, Irvine, California 92697, United States
| | - Jung-Hun Woo
- Department of Civil and Environmental Engineering, Konkuk University, Seoul 05029, Republic of Korea
| | - Younha Kim
- Energy, Climate, and Environment (ECE) Program, International Institute for Applied Systems Analysis (IIASA), Laxenburg A-2361, Austria
| | - Ronald C Cohen
- Department of Chemistry, University of California, Berkeley, California 94720, United States
- Department of Earth & Planetary Sciences, University of California, Berkeley CA 94 720, United States
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Wang K, Zhang Y, Yu S, Wong DC, Pleim J, Mathur R, Kelly JT, Bell M. A comparative study of two-way and offline coupled WRF v3.4 and CMAQ v5.0.2 over the contiguous US: performance evaluation and impacts of chemistry-meteorology feedbacks on air quality. GEOSCIENTIFIC MODEL DEVELOPMENT 2021; 14:7189-7221. [PMID: 35237388 DOI: 10.5194/gmd-2020-218] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
The two-way coupled Weather Research and Forecasting and Community Multiscale Air Quality (WRF-CMAQ) model has been developed to more realistically represent the atmosphere by accounting for complex chemistry-meteorology feedbacks. In this study, we present a comparative analysis of two-way (with consideration of both aerosol direct and indirect effects) and offline coupled WRF v3.4 and CMAQ v5.0.2 over the contiguous US. Long-term (5 years from 2008 to 2012) simulations using WRF-CMAQ with both offline and two-way coupling modes are carried out with anthropogenic emissions based on multiple years of the U.S. National Emission Inventory and chemical initial and boundary conditions derived from an advanced Earth system model (i.e., a modified version of the Community Earth System Model/Community Atmospheric Model). The comprehensive model evaluations show that both two-way WRF-CMAQ and WRF-only simulations perform well for major meteorological variables such as temperature at 2 m, relative humidity at 2 m, wind speed at 10 m, precipitation (except for against the National Climatic Data Center data), and shortwave and longwave radiation. Both two-way and offline CMAQ also show good performance for ozone (O3) and fine particulate matter (PM2.5). Due to the consideration of aerosol direct and indirect effects, two-way WRF-CMAQ shows improved performance over offline coupled WRF and CMAQ in terms of spatiotemporal distributions and statistics, especially for radiation, cloud forcing, O3, sulfate, nitrate, ammonium, elemental carbon, tropospheric O3 residual, and column nitrogen dioxide (NO2). For example, the mean biases have been reduced by more than 10 W m-2 for shortwave radiation and cloud radiative forcing and by more than 2 ppb for max 8 h O3. However, relatively large biases still exist for cloud predictions, some PM2.5 species, and PM10 that warrant follow-up studies to better understand those issues. The impacts of chemistry-meteorological feedbacks are found to play important roles in affecting regional air quality in the US by reducing domain-average concentrations of carbon monoxide (CO), O3, nitrogen oxide (NO x ), volatile organic compounds (VOCs), and PM2.5 by 3.1% (up to 27.8%), 4.2% (up to 16.2%), 6.6% (up to 50.9%), 5.8% (up to 46.6%), and 8.6% (up to 49.1%), respectively, mainly due to reduced radiation, temperature, and wind speed. The overall performance of the two-way coupled WRF-CMAQ model achieved in this work is generally good or satisfactory and the improved performance for two-way coupled WRF-CMAQ should be considered along with other factors in developing future model applications to inform policy making.
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Affiliation(s)
- Kai Wang
- Department of Civil and Environmental Engineering, Northeastern University, Boston, MA 02115, USA
| | - Yang Zhang
- Department of Civil and Environmental Engineering, Northeastern University, Boston, MA 02115, USA
| | - Shaocai Yu
- Key Laboratory of Environmental Remediation and Ecological Health, Ministry of Education; Research Center for Air Pollution and Health, College of Environment and Resource Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, P.R. China
| | - David C Wong
- Center for Environmental Measurement and Modeling, U.S. EPA, Research Triangle Park, NC 27711, USA
| | - Jonathan Pleim
- Center for Environmental Measurement and Modeling, U.S. EPA, Research Triangle Park, NC 27711, USA
| | - Rohit Mathur
- Center for Environmental Measurement and Modeling, U.S. EPA, Research Triangle Park, NC 27711, USA
| | - James T Kelly
- Office of Air Quality Planning and Standards, U.S. EPA, Research Triangle Park, NC 27711, USA
| | - Michelle Bell
- School of Forestry & Environmental Studies, Yale University, New Haven, CT 06511, USA
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9
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Wang K, Zhang Y, Yu S, Wong DC, Pleim J, Mathur R, Kelly JT, Bell M. A comparative study of two-way and offline coupled WRF v3.4 and CMAQ v5.0.2 over the contiguous US: performance evaluation and impacts of chemistry-meteorology feedbacks on air quality. GEOSCIENTIFIC MODEL DEVELOPMENT 2021; 14:7189-7221. [PMID: 35237388 PMCID: PMC8883479 DOI: 10.5194/gmd-14-7189-2021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The two-way coupled Weather Research and Forecasting and Community Multiscale Air Quality (WRF-CMAQ) model has been developed to more realistically represent the atmosphere by accounting for complex chemistry-meteorology feedbacks. In this study, we present a comparative analysis of two-way (with consideration of both aerosol direct and indirect effects) and offline coupled WRF v3.4 and CMAQ v5.0.2 over the contiguous US. Long-term (5 years from 2008 to 2012) simulations using WRF-CMAQ with both offline and two-way coupling modes are carried out with anthropogenic emissions based on multiple years of the U.S. National Emission Inventory and chemical initial and boundary conditions derived from an advanced Earth system model (i.e., a modified version of the Community Earth System Model/Community Atmospheric Model). The comprehensive model evaluations show that both two-way WRF-CMAQ and WRF-only simulations perform well for major meteorological variables such as temperature at 2 m, relative humidity at 2 m, wind speed at 10 m, precipitation (except for against the National Climatic Data Center data), and shortwave and longwave radiation. Both two-way and offline CMAQ also show good performance for ozone (O3) and fine particulate matter (PM2.5). Due to the consideration of aerosol direct and indirect effects, two-way WRF-CMAQ shows improved performance over offline coupled WRF and CMAQ in terms of spatiotemporal distributions and statistics, especially for radiation, cloud forcing, O3, sulfate, nitrate, ammonium, elemental carbon, tropospheric O3 residual, and column nitrogen dioxide (NO2). For example, the mean biases have been reduced by more than 10 W m-2 for shortwave radiation and cloud radiative forcing and by more than 2 ppb for max 8 h O3. However, relatively large biases still exist for cloud predictions, some PM2.5 species, and PM10 that warrant follow-up studies to better understand those issues. The impacts of chemistry-meteorological feedbacks are found to play important roles in affecting regional air quality in the US by reducing domain-average concentrations of carbon monoxide (CO), O3, nitrogen oxide (NO x ), volatile organic compounds (VOCs), and PM2.5 by 3.1% (up to 27.8%), 4.2% (up to 16.2%), 6.6% (up to 50.9%), 5.8% (up to 46.6%), and 8.6% (up to 49.1%), respectively, mainly due to reduced radiation, temperature, and wind speed. The overall performance of the two-way coupled WRF-CMAQ model achieved in this work is generally good or satisfactory and the improved performance for two-way coupled WRF-CMAQ should be considered along with other factors in developing future model applications to inform policy making.
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Affiliation(s)
- Kai Wang
- Department of Civil and Environmental Engineering, Northeastern University, Boston, MA 02115, USA
| | - Yang Zhang
- Department of Civil and Environmental Engineering, Northeastern University, Boston, MA 02115, USA
| | - Shaocai Yu
- Key Laboratory of Environmental Remediation and Ecological Health, Ministry of Education; Research Center for Air Pollution and Health, College of Environment and Resource Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, P.R. China
| | - David C. Wong
- Center for Environmental Measurement and Modeling, U.S. EPA, Research Triangle Park, NC 27711, USA
| | - Jonathan Pleim
- Center for Environmental Measurement and Modeling, U.S. EPA, Research Triangle Park, NC 27711, USA
| | - Rohit Mathur
- Center for Environmental Measurement and Modeling, U.S. EPA, Research Triangle Park, NC 27711, USA
| | - James T. Kelly
- Office of Air Quality Planning and Standards, U.S. EPA, Research Triangle Park, NC 27711, USA
| | - Michelle Bell
- School of Forestry & Environmental Studies, Yale University, New Haven, CT 06511, USA
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10
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Sun X, Ivey CE, Baker KR, Nenes A, Lareau NP, Holmes HA. Confronting Uncertainties of Simulated Air Pollution Concentrations during Persistent Cold Air Pool Events in the Salt Lake Valley, Utah. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2021; 55:15072-15081. [PMID: 34709803 PMCID: PMC9585943 DOI: 10.1021/acs.est.1c05467] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Abstract
Air pollutant accumulations during wintertime persistent cold air pool (PCAP) events in mountain valleys are of great concern for public health worldwide. Uncertainties associated with the simulated meteorology under stable conditions over complex terrain hinder realistic simulations of air quality using chemical transport models. We use the Community Multiscale Air Quality (CMAQ) model to simulate the gaseous and particulate species for 1 month in January 2011 during the Persistent Cold Air Pool Study (PCAPS) in the Salt Lake Valley (SLV), Utah (USA). Results indicate that the temporal variability associated with the elevated NOx and PM2.5 concentrations during PCAP events was captured by the model (r = 0.20 for NOx and r = 0.49 for PM2.5). However, concentrations were not at the correct magnitude (NMB = -35/12% for PM2.5 during PCAPs/non-PCAPs), where PM2.5 was underestimated during PCAP events and overestimated during non-PCAP periods. The underestimated PCAP strength is represented by valley heat deficit, which contributed to the underestimated PM2.5 concentrations compared with observations due to the model simulating more vertical mixing and less stable stratification than what was observed. Based on the observations, the dominant PM2.5 species were ammonium and nitrate. We provide a discussion that aims to investigate the emissions and chemistry model uncertainties using the nitrogen ratio method and the thermodynamic ammonium nitrate regime method.
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Affiliation(s)
- Xia Sun
- Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, CO 80309, United States
- NOAA Global Systems Laboratory, Boulder, CO 80305, United States
| | - Cesunica E. Ivey
- Civil and Environmental Engineering University of California, Berkeley, CA 94720, United States
| | - Kirk R. Baker
- U.S. Environmental Protection Agency, Research Triangle Park, NC 27703, United States
| | - Athanasios Nenes
- Institute for Chemical Engineering Sciences, Foundation for Research and Technology Hellas, Patras, GR-26504, Greece
- School of Architecture, Civil & Environmental Engineering, Ecole Polytechnique Fédérale de Lausanne, CH-1015, Lausanne, Switzerland
| | - Neil P. Lareau
- Atmospheric Sciences Program, Department of Physics, University of Nevada, Reno, NV 89557, United States
| | - Heather A. Holmes
- Department of Chemical Engineering, University of Utah, Salt Lake City, UT 84112, United States
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11
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Hood RR, Shenk GW, Dixon RL, Smith SMC, Ball WP, Bash JO, Batiuk R, Boomer K, Brady DC, Cerco C, Claggett P, de Mutsert K, Easton ZM, Elmore AJ, Friedrichs MAM, Harris LA, Ihde TF, Lacher I, Li L, Linker LC, Miller A, Moriarty J, Noe GB, Onyullo G, Rose K, Skalak K, Tian R, Veith TL, Wainger L, Weller D, Zhang YJ. The Chesapeake Bay Program Modeling System: Overview and Recommendations for Future Development. Ecol Modell 2021; 465:1-109635. [PMID: 34675451 PMCID: PMC8525429 DOI: 10.1016/j.ecolmodel.2021.109635] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
The Chesapeake Bay is the largest, most productive, and most biologically diverse estuary in the continental United States providing crucial habitat and natural resources for culturally and economically important species. Pressures from human population growth and associated development and agricultural intensification have led to excessive nutrient and sediment inputs entering the Bay, negatively affecting the health of the Bay ecosystem and the economic services it provides. The Chesapeake Bay Program (CBP) is a unique program formally created in 1983 as a multi-stakeholder partnership to guide and foster restoration of the Chesapeake Bay and its watershed. Since its inception, the CBP Partnership has been developing, updating, and applying a complex linked modeling system of watershed, airshed, and estuary models as a planning tool to inform strategic management decisions and Bay restoration efforts. This paper provides a description of the 2017 CBP Modeling System and the higher trophic level models developed by the NOAA Chesapeake Bay Office, along with specific recommendations that emerged from a 2018 workshop designed to inform future model development. Recommendations highlight the need for simulation of watershed inputs, conditions, processes, and practices at higher resolution to provide improved information to guide local nutrient and sediment management plans. More explicit and extensive modeling of connectivity between watershed landforms and estuary sub-areas, estuarine hydrodynamics, watershed and estuarine water quality, the estuarine-watershed socioecological system, and living resources will be important to broaden and improve characterization of responses to targeted nutrient and sediment load reductions. Finally, the value and importance of maintaining effective collaborations among jurisdictional managers, scientists, modelers, support staff, and stakeholder communities is emphasized. An open collaborative and transparent process has been a key element of successes to date and is vitally important as the CBP Partnership moves forward with modeling system improvements that help stakeholders evolve new knowledge, improve management strategies, and better communicate outcomes.
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Affiliation(s)
- Raleigh R Hood
- Horn Point Laboratory, University of Maryland Center for Environmental Science, P.O. Box 775, Cambridge, MD 21613, USA
| | - Gary W Shenk
- USGS Chesapeake Bay Program Office, 410 Severn Avenue, Suite 109, Annapolis, MD, 21403, USA
| | - Rachel L Dixon
- Chesapeake Research Consortium, 645 Contees Wharf Road, Edgewater, MD 21037, USA
| | - Sean M C Smith
- University of Maine, School of Earth and Climate Sciences, Bryand Global Science Center, Orono, ME 04469, USA
| | - William P Ball
- Chesapeake Research Consortium, 645 Contees Wharf Road, Edgewater, MD 21037, USA
| | - Jesse O Bash
- Environmental Protection Agency, Center for Environmental Measurement and Modeling, 109 T.W. Alexander Drive, Durham, NC 27709, USA
| | - Rich Batiuk
- U.S. Environmental Protection Agency, Chesapeake Bay Program Office, 410 Severn Avenue, Suite 109, Annapolis, MD, 21403, USA
| | - Kathy Boomer
- The Nature Conservancy, 114 South Washington Street, Easton, MD 21601, USA
| | - Damian C Brady
- Darling Marine Center, University of Maine, 193 Clarks Cove Rd, Walpole, ME 04573, USA
| | - Carl Cerco
- #U.S. Army Corps of Engineers Waterways Experiment Station, P.O. Box 631, Vicksburg, MS 39180, USA
| | - Peter Claggett
- USGS Chesapeake Bay Program Office, 410 Severn Avenue, Suite 109, Annapolis, MD, 21403, USA
| | - Kim de Mutsert
- University of Southern Mississippi, Gulf Coast Research Laboratory, 703 East Beach Drive, Ocean Springs, MS 39564, USA
| | | | - Andrew J Elmore
- Appalachian Laboratory, University of Maryland Center for Environmental Science, 301 Braddock Rd, Frostburg, MD 21532, USA
| | - Marjorie A M Friedrichs
- Virginia Institute of Marine Science, William & Mary, 1375 Greate Rd, Gloucester Point, VA 23062, USA
| | - Lora A Harris
- Chesapeake Biological Laboratory, University of Maryland Center for Environmental Science, P.O. Box 38, Solomons, MD 20688, USA
| | - Thomas F Ihde
- Patuxent Environmental & Aquatic Research Laboratory, Morgan State University, 10545 Mackall Road, St. Leonard, MD 20685, USA
| | - Iara Lacher
- Smithsonian Conservation Biology Institute, 1500 Remount Rd, Front Royal, VA 22630 USA
| | - Li Li
- Department of Civil and Environmental Engineering, Penn State University, University Park, PA 16802, USA
| | - Lewis C Linker
- U.S. Environmental Protection Agency, Chesapeake Bay Program Office, 410 Severn Avenue, Suite 109, Annapolis, MD, 21403, USA
| | - Andrew Miller
- Department of Geography and Environmental Systems, University of Maryland Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250, USA
| | - Julia Moriarty
- Institute for Arctic and Alpine Research, Department of Atmospheric and Oceanic Sciences, University of Colorado, Boulder CO 80309, USA
| | - Gregory B Noe
- Florence Bascom Geoscience Center, U.S. Geological Survey, 12201 Sunrise Valley Drive, MS926A, Reston, VA 20192, USA
| | - George Onyullo
- District of Columbia Department of Energy and Environment, 1200 First Street NE, Washington DC 20002, USA
| | - Kenneth Rose
- Horn Point Laboratory, University of Maryland Center for Environmental Science, P.O. Box 775, Cambridge, MD 21613, USA
| | - Katie Skalak
- National Research Program, U.S. Geological Survey, 12201Sunrise Valley Drive, Reston, VA 20192, USA
| | - Richard Tian
- USGS Chesapeake Bay Program Office, 410 Severn Avenue, Suite 109, Annapolis, MD, 21403, USA
| | - Tamie L Veith
- U.S. Department of Agriculture Agricultural Research Service, Pasture Systems and Watershed Management Research Unit, Building 3702, Curtin Road, University Park, PA 16802, USA
| | - Lisa Wainger
- Chesapeake Biological Laboratory, University of Maryland Center for Environmental Science, P.O. Box 38, Solomons, MD 20688, USA
| | - Donald Weller
- Smithsonian Environmental Research Center, 647 Contees Wharf Road, Edgewater, MD 21037, USA
| | - Yinglong Joseph Zhang
- Virginia Institute of Marine Science, William & Mary, 1375 Greate Rd, Gloucester Point, VA 23062, USA
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12
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Kelly JT, Jang C, Zhu Y, Long S, Xing J, Wang S, Murphy BN, Pye HOT. Predicting the Nonlinear Response of PM 2.5 and Ozone to Precursor Emission Changes with a Response Surface Model. ATMOSPHERE 2021; 12:1-1044. [PMID: 34567797 PMCID: PMC8459679 DOI: 10.3390/atmos12081044] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Reducing PM2.5 and ozone concentrations is important to protect human health and the environment. Chemical transport models, such as the Community Multiscale Air Quality (CMAQ) model, are valuable tools for exploring policy options for improving air quality but are computationally expensive. Here, we statistically fit an efficient polynomial function in a response surface model (pf-RSM) to CMAQ simulations over the eastern U.S. for January and July 2016. The pf-RSM predictions were evaluated using out-of-sample CMAQ simulations and used to examine the nonlinear response of air quality to emission changes. Predictions of the pf-RSM are in good agreement with the out-of-sample CMAQ simulations, with some exceptions for cases with anthropogenic emission reductions approaching 100%. NOX emission reductions were more effective for reducing PM2.5 and ozone concentrations than SO2, NH3, or traditional VOC emission reductions. NH3 emission reductions effectively reduced nitrate concentrations in January but increased secondary organic aerosol (SOA) concentrations in July. More work is needed on SOA formation under conditions of low NH3 emissions to verify the responses of SOA to NH3 emission changes predicted here. Overall, the pf-RSM performs well in the eastern U.S., but next-generation RSMs based on deep learning may be needed to meet the computational requirements of typical regulatory applications.
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Affiliation(s)
- James T. Kelly
- Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, Durham, NC 27711, USA
| | - Carey Jang
- Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, Durham, NC 27711, USA
| | - Yun Zhu
- School of Environment and Energy, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
| | - Shicheng Long
- School of Environment and Energy, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
| | - Jia Xing
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Shuxiao Wang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Benjamin N. Murphy
- Center for Environmental Measurement and Modeling, U.S. Environmental Protection Agency, Research Triangle Park, Durham, NC 27711, USA
| | - Havala O. T. Pye
- Center for Environmental Measurement and Modeling, U.S. Environmental Protection Agency, Research Triangle Park, Durham, NC 27711, USA
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13
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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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14
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Murphy BN, Nolte CG, Sidi F, Bash JO, Appel KW, Jang C, Kang D, Kelly J, Mathur R, Napelenok S, Pouliot G, Pye HOT. The Detailed Emissions Scaling, Isolation, and Diagnostic (DESID) module in the Community Multiscale Air Quality (CMAQ) modeling system version 5.3.2. GEOSCIENTIFIC MODEL DEVELOPMENT 2021; 14:3407-3420. [PMID: 34336142 PMCID: PMC8318114 DOI: 10.5194/gmd-14-3407-2021] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Abstract
Air quality modeling for research and regulatory applications often involves executing many emissions sensitivity cases to quantify impacts of hypothetical scenarios, estimate source contributions, or quantify uncertainties. Despite the prevalence of this task, conventional approaches for perturbing emissions in chemical transport models like the Community Multiscale Air Quality (CMAQ) model require extensive offline creation and finalization of alternative emissions input files. This workflow is often time-consuming, error-prone, inconsistent among model users, difficult to document, and dependent on increased hard disk resources. The Detailed Emissions Scaling, Isolation, and Diagnostic (DESID) module, a component of CMAQv5.3 and beyond, addresses these limitations by performing these modifications online during the air quality simulation. Further, the model contains an Emission Control Interface which allows users to prescribe both simple and highly complex emissions scaling operations with control over individual or multiple chemical species, emissions sources, and spatial areas of interest. DESID further enhances the transparency of its operations with extensive error-checking and optional gridded output of processed emission fields. These new features are of high value to many air quality applications including routine perturbation studies, atmospheric chemistry research, and coupling with external models (e.g., energy system models, reduced-form models).
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Affiliation(s)
- Benjamin N. Murphy
- Center for Environmental Measurement and Modeling, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Christopher G. Nolte
- Center for Environmental Measurement and Modeling, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Fahim Sidi
- Center for Environmental Measurement and Modeling, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Jesse O. Bash
- Center for Environmental Measurement and Modeling, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - K. Wyat Appel
- Center for Environmental Measurement and Modeling, 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
| | - Daiwen Kang
- Center for Environmental Measurement and Modeling, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - James Kelly
- Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Rohit Mathur
- Center for Environmental Measurement and Modeling, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Sergey Napelenok
- Center for Environmental Measurement and Modeling, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - George Pouliot
- Center for Environmental Measurement and Modeling, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Havala O. T. Pye
- Center for Environmental Measurement and Modeling, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
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15
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Murphy BN, Nolte CG, Sidi F, Bash JO, Appel KW, Jang C, Kang D, Kelly J, Mathur R, Napelenok S, Pouliot G, Pye HOT. The Detailed Emissions Scaling, Isolation, and Diagnostic (DESID) module in the Community Multiscale Air Quality (CMAQ) modeling system version 5.3.2. GEOSCIENTIFIC MODEL DEVELOPMENT 2021. [PMID: 34336142 DOI: 10.5194/gmd-2020-361] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Air quality modeling for research and regulatory applications often involves executing many emissions sensitivity cases to quantify impacts of hypothetical scenarios, estimate source contributions, or quantify uncertainties. Despite the prevalence of this task, conventional approaches for perturbing emissions in chemical transport models like the Community Multiscale Air Quality (CMAQ) model require extensive offline creation and finalization of alternative emissions input files. This workflow is often time-consuming, error-prone, inconsistent among model users, difficult to document, and dependent on increased hard disk resources. The Detailed Emissions Scaling, Isolation, and Diagnostic (DESID) module, a component of CMAQv5.3 and beyond, addresses these limitations by performing these modifications online during the air quality simulation. Further, the model contains an Emission Control Interface which allows users to prescribe both simple and highly complex emissions scaling operations with control over individual or multiple chemical species, emissions sources, and spatial areas of interest. DESID further enhances the transparency of its operations with extensive error-checking and optional gridded output of processed emission fields. These new features are of high value to many air quality applications including routine perturbation studies, atmospheric chemistry research, and coupling with external models (e.g., energy system models, reduced-form models).
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Affiliation(s)
- Benjamin N Murphy
- Center for Environmental Measurement and Modeling, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Christopher G Nolte
- Center for Environmental Measurement and Modeling, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Fahim Sidi
- Center for Environmental Measurement and Modeling, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Jesse O Bash
- Center for Environmental Measurement and Modeling, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - K Wyat Appel
- Center for Environmental Measurement and Modeling, 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
| | - Daiwen Kang
- Center for Environmental Measurement and Modeling, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - James Kelly
- Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Rohit Mathur
- Center for Environmental Measurement and Modeling, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Sergey Napelenok
- Center for Environmental Measurement and Modeling, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - George Pouliot
- Center for Environmental Measurement and Modeling, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Havala O T Pye
- Center for Environmental Measurement and Modeling, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
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16
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Hunt SW, Winner DA, Wesson K, Kelly JT. Furthering a partnership: Air quality modeling and improving public health. JOURNAL OF THE AIR & WASTE MANAGEMENT ASSOCIATION (1995) 2021; 71:682-688. [PMID: 33443461 PMCID: PMC8318118 DOI: 10.1080/10962247.2021.1876180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Revised: 01/05/2021] [Accepted: 01/05/2021] [Indexed: 06/12/2023]
Abstract
Air pollution is one of the top five risk factors for population health globally. In recent years, advances in air pollution data and modeling have occurred simultaneously with advances in data and methods available for health studies. To realize the potential of such advances, the air quality modeling and public health communities should continue to strengthen their engagements and build effective interdisciplinary teams. These partnerships recognize the tight coupling between air quality and health data and methods and the value of expertise from multiple fields to ensure that this information is applied appropriately with a deep understanding of its capabilities and limitations. Building effective multidisciplinary teams takes a sustained commitment to engage with partners with different expertise to establish working partnerships and collaborations to better address public exposures to air pollution. Effective partnerships enable better targeting of research resources to answer important questions and provide essential information to protect public health.Implications: Air quality models are an effective tool that can be used to estimate air pollution exposure in epidemiologic studies and risk assessments. Working together in collaborative multidisciplinary teams will lead to greater advancements in understanding of air pollution impacts and in useful information informing actions to improve public health.
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Affiliation(s)
- Sherri W Hunt
- Immediate Office of the Assistant Administrator , Office of Research and Development, U.S. Environmental Protection Agency, Washington, USA
| | - Darrell A Winner
- Immediate Office of the Assistant Administrator , Office of Research and Development, U.S. Environmental Protection Agency, Washington, USA
| | - Karen Wesson
- Office of Air Quality Planning and Standards, Office of Air and Radiation, U.S. Environmental Protection Agency, Research Triangle Park, USA
| | - James T Kelly
- Office of Air Quality Planning and Standards, Office of Air and Radiation, U.S. Environmental Protection Agency, Research Triangle Park, USA
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Appel KW, Bash JO, Fahey KM, Foley KM, Gilliam RC, Hogrefe C, Hutzell WT, Kang D, Mathur R, Murphy BN, Napelenok SL, Nolte CG, Pleim JE, Pouliot GA, Pye HOT, Ran L, Roselle SJ, Sarwar G, Schwede DB, Sidi FI, Spero TL, Wong DC. The Community Multiscale Air Quality (CMAQ) model versions 5.3 and 5.3.1: system updates and evaluation. GEOSCIENTIFIC MODEL DEVELOPMENT 2021; 14:2867-2897. [PMID: 34676058 PMCID: PMC8525427 DOI: 10.5194/gmd-14-2867-2021] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
The Community Multiscale Air Quality (CMAQ) model version 5.3 (CMAQ53), released to the public in August 2019 and followed by version 5.3.1 (CMAQ531) in December 2019, contains numerous science updates, enhanced functionality, and improved computation efficiency relative to the previous version of the model, 5.2.1 (CMAQ521). Major science advances in the new model include a new aerosol module (AERO7) with significant updates to secondary organic aerosol (SOA) chemistry, updated chlorine chemistry, updated detailed bromine and iodine chemistry, updated simple halogen chemistry, the addition of dimethyl sulfide (DMS) chemistry in the CB6r3 chemical mechanism, updated M3Dry bidirectional deposition model, and the new Surface Tiled Aerosol and Gaseous Exchange (STAGE) bidirectional deposition model. In addition, support for the Weather Research and Forecasting (WRF) model's hybrid vertical coordinate (HVC) was added to CMAQ53 and the Meteorology-Chemistry Interface Processor (MCIP) version 5.0 (MCIP50). Enhanced functionality in CMAQ53 includes the new Detailed Emissions Scaling, Isolation and Diagnostic (DESID) system for scaling incoming emissions to CMAQ and reading multiple gridded input emission files. Evaluation of CMAQ531 was performed by comparing monthly and seasonal mean daily 8 h average (MDA8) O3 and daily PM2.5 values from several CMAQ531 simulations to a similarly configured CMAQ521 simulation encompassing 2016. For MDA8 O3, CMAQ531 has higher O3 in the winter versus CMAQ521, due primarily to reduced dry deposition to snow, which strongly reduces wintertime O3 bias (2-4 ppbv monthly average). MDA8 O3 is lower with CMAQ531 throughout the rest of the year, particularly in spring, due in part to reduced O3 from the lateral boundary conditions (BCs), which generally increases MDA8 O3 bias in spring and fall ( 0.5 μg m-3). For daily 24 h average PM2.5, CMAQ531 has lower concentrations on average in spring and fall, higher concentrations in summer, and similar concentrations in winter to CMAQ521, which slightly increases bias in spring and fall and reduces bias in summer. Comparisons were also performed to isolate updates to several specific aspects of the modeling system, namely the lateral BCs, meteorology model version, and the deposition model used. Transitioning from a hemispheric CMAQ (HCMAQ) version 5.2.1 simulation to a HCMAQ version 5.3 simulation to provide lateral BCs contributes to higher O3 mixing ratios in the regional CMAQ simulation in higher latitudes during winter (due to the decreased O3 dry deposition to snow in CMAQ53) and lower O3 mixing ratios in middle and lower latitudes year-round (due to reduced O3 over the ocean with CMAQ53). Transitioning from WRF version 3.8 to WRF version 4.1.1 with the HVC resulted in consistently higher (1.0-1.5 ppbv) MDA8 O3 mixing ratios and higher PM2.5 concentrations (0.1-0.25 μg m-3) throughout the year. Finally, comparisons of the M3Dry and STAGE deposition models showed that MDA8 O3 is generally higher with M3Dry outside of summer, while PM2.5 is consistently higher with STAGE due to differences in the assumptions of particle deposition velocities to non-vegetated surfaces and land use with short vegetation (e.g., grasslands) between the two models. For ambient NH3, STAGE has slightly higher concentrations and smaller bias in the winter, spring, and fall, while M3Dry has higher concentrations and smaller bias but larger error and lower correlation in the summer.
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Affiliation(s)
- K. Wyat Appel
- Center for Environmental Measurement and Modeling, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Jesse O. Bash
- Center for Environmental Measurement and Modeling, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Kathleen M. Fahey
- Center for Environmental Measurement and Modeling, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Kristen M. Foley
- Center for Environmental Measurement and Modeling, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Robert C. Gilliam
- Center for Environmental Measurement and Modeling, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Christian Hogrefe
- Center for Environmental Measurement and Modeling, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - William T. Hutzell
- Center for Environmental Measurement and Modeling, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Daiwen Kang
- Center for Environmental Measurement and Modeling, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Rohit Mathur
- Center for Environmental Measurement and Modeling, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Benjamin N. Murphy
- Center for Environmental Measurement and Modeling, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Sergey L. Napelenok
- Center for Environmental Measurement and Modeling, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Christopher G. Nolte
- Center for Environmental Measurement and Modeling, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Jonathan E. Pleim
- Center for Environmental Measurement and Modeling, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - George A. Pouliot
- Center for Environmental Measurement and Modeling, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Havala O. T. Pye
- Center for Environmental Measurement and Modeling, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Limei Ran
- Center for Environmental Measurement and Modeling, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Shawn J. Roselle
- Center for Environmental Measurement and Modeling, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Golam Sarwar
- Center for Environmental Measurement and Modeling, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Donna B. Schwede
- Center for Environmental Measurement and Modeling, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Fahim I. Sidi
- Center for Environmental Measurement and Modeling, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Tanya L. Spero
- Center for Environmental Measurement and Modeling, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - David C. Wong
- Center for Environmental Measurement and Modeling, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
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18
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Toro C, Foley K, Simon H, Henderson B, Baker KR, Eyth A, Timin B, Appel W, Luecken D, Beardsley M, Sonntag D, Possiel N, Roberts S. Evaluation of 15 years of modeled atmospheric oxidized nitrogen compounds across the contiguous United States. ELEMENTA (WASHINGTON, D.C.) 2021; 9:10.1525/elementa.2020.00158. [PMID: 34017874 PMCID: PMC8128711 DOI: 10.1525/elementa.2020.00158] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Atmospheric nitrogen oxide and nitrogen dioxide (NO + NO2, together termed as NO X ) estimates from annual photochemical simulations for years 2002-2016 are compared to surface network measurements of NO X and total gas-phase-oxidized reactive nitrogen (NO Y ) to evaluate the Community Multiscale Air Quality (CMAQ) modeling system performance by U.S. region, season, and time of day. In addition, aircraft measurements from 2011 Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality are used to evaluate how emissions, chemical mechanism, and measurement uncertainty each contribute to the overall model performance. We show distinct seasonal and time-of-day patterns in NO X performance. Summertime NO X is overpredicted with bimodal peaks in bias during early morning and evening hours and persisting overnight. The summertime morning NO X bias dropped from between 28% and 57% for earlier years (2002-2012) to between -2% and 7% for later years (2013-2016). Summer daytime NO X tends to be unbiased or underpredicted. In winter, the evening NO X overpredictions remain, but NO X is unbiased or underpredicted overnight, in the morning, and during the day. NO X overpredictions are most pronounced in the Midwestern and Southern United States with Western regions having more of a tendency toward model underpredictions of NO X . Modeled NO X performance has improved substantially over time, reflecting updates to the emission inputs and the CMAQ air quality model. Model performance improvements are largest for years simulated with CMAQv5.1 or later and for emission inventory years 2014 and later, coinciding with reduced onroad NO X emissions from vehicles with newer emission control technologies and improved treatment of chemistry, deposition, and vertical mixing in CMAQ. Our findings suggest that emissions temporalization of specific mobile source sectors have a small impact on model performance, while chemistry updates improve predictions of NO Y but do not improve summertime NO X bias in the Baltimore/DC area. Sensitivity runs performed for different locations across the country suggest that the improvement in summer NO X performance can be attributed to updates in vertical mixing incorporated in CMAQv5.1.
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Affiliation(s)
- Claudia Toro
- U.S. Environmental Protection Agency, Ann Arbor, MI, USA
| | - Kristen Foley
- U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Heather Simon
- U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Barron Henderson
- U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Kirk R. Baker
- U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Alison Eyth
- U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Brian Timin
- U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Wyat Appel
- U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Deborah Luecken
- U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | | | | | - Norm Possiel
- U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Sarah Roberts
- U.S. Environmental Protection Agency, Ann Arbor, MI, USA
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19
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Kelly JT, Jang C, Timin B, Di Q, Schwartz J, Liu Y, van Donkelaar A, Martin RV, Berrocal V, Bell ML. Examining PM 2.5 concentrations and exposure using multiple models. ENVIRONMENTAL RESEARCH 2021; 196:110432. [PMID: 33166538 PMCID: PMC8102649 DOI: 10.1016/j.envres.2020.110432] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Revised: 10/22/2020] [Accepted: 11/03/2020] [Indexed: 05/07/2023]
Abstract
Epidemiologic studies have found associations between fine particulate matter (PM2.5) exposure and adverse health effects using exposure models that incorporate monitoring data and other relevant information. Here, we use nine PM2.5 concentration models (i.e., exposure models) that span a wide range of methods to investigate i) PM2.5 concentrations in 2011, ii) potential changes in PM2.5 concentrations between 2011 and 2028 due to on-the-books regulations, and iii) PM2.5 exposure for the U.S. population and four racial/ethnic groups. The exposure models included two geophysical chemical transport models (CTMs), two interpolation methods, a satellite-derived aerosol optical depth-based method, a Bayesian statistical regression model, and three data-rich machine learning methods. We focused on annual predictions that were regridded to 12-km resolution over the conterminous U.S., but also considered 1-km predictions in sensitivity analyses. The exposure models predicted broadly consistent PM2.5 concentrations, with relatively high concentrations on average over the eastern U.S. and greater variability in the western U.S. However, differences in national concentration distributions (median standard deviation: 1.00 μg m-3) and spatial distributions over urban areas were evident. Further exploration of these differences and their implications for specific applications would be valuable. PM2.5 concentrations were estimated to decrease by about 1 μg m-3 on average due to modeled emission changes between 2011 and 2028, with decreases of more than 3 μg m-3 in areas with relatively high 2011 concentrations that were projected to experience relatively large emission reductions. Agreement among models was closer for population-weighted than uniformly weighted averages across the domain. About 50% of the population was estimated to experience PM2.5 concentrations less than 10 μg m-3 in 2011 and PM2.5 improvements of about 2 μg m-3 due to modeled emission changes between 2011 and 2028. Two inequality metrics were used to characterize differences in exposure among the four racial/ethnic groups. The metrics generally yielded consistent information and suggest that the modeled emission reductions between 2011 and 2028 would reduce absolute exposure inequality on average.
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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, USA.
| | - Carey Jang
- Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Brian Timin
- Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Qian Di
- Vanke School of Public Health, Tsinghua University, Beijing, China
| | - Joel Schwartz
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Yang Liu
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Aaron van Donkelaar
- Department of Energy, Environmental & Chemical Engineering, Washington University, St. Louis, MO, USA; Department of Physics and Atmospheric Science, Dalhousie University, Halifax, Nova Scotia, Canada
| | - Randall V Martin
- Department of Energy, Environmental & Chemical Engineering, Washington University, St. Louis, MO, USA; Department of Physics and Atmospheric Science, Dalhousie University, Halifax, Nova Scotia, Canada; Harvard-Smithsonian Centre for Astrophysics, Cambridge, MA, USA
| | - Veronica Berrocal
- Donald Bren School of Information and Computer Sciences, University of California, Irvine, CA, USA
| | - Michelle L Bell
- School of the Environment, Yale University, New Haven, CT, USA
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20
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Meskhidze N, Sutherland B, Ling X, Dawson K, Johnson MS, Henderson B, Hostetler CA, Ferrare RA. Improving Estimates of PM 2.5 Concentration and Chemical Composition by Application of High Spectral Resolution Lidar (HSRL) and Creating Aerosol Types from Chemistry (CATCH) Algorithm. ATMOSPHERIC ENVIRONMENT (OXFORD, ENGLAND : 1994) 2021; 250:10.1016/j.atmosenv.2021.118250. [PMID: 34381305 PMCID: PMC8353958 DOI: 10.1016/j.atmosenv.2021.118250] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Improved characterization of ambient PM2.5 mass concentration and chemical speciation is a topic of interest in air quality and climate sciences. Over the past decades, considerable efforts have been made to improve ground-level PM2.5 using remotely sensed data. Here we present two new approaches for estimating atmospheric PM2.5 and chemical composition based on the High Spectral Resolution Lidar (HSRL)-retrieved aerosol extinction values and types and Creating Aerosol Types from Chemistry (CATCH)-derived aerosol chemical composition. The first methodology (CMAQ-HSRL-CH) improves EPA's Community Multiscale Air Quality (CMAQ) predictions by applying variable scaling factors derived using remotely-sensed information about aerosol vertical distribution and types and the CATCH algorithm. The second methodology (HSRL-CH) does not require regional model runs and can provide atmospheric PM2.5 mass concentration and chemical speciation using only the remotely sensed data and the CATCH algorithm. The resulting PM2.5 concentrations and chemical speciation derived for NASA DISCOVER-AQ (Deriving Information on Surface Conditions from COlumn and VERtically Resolved Observations Relevant to Air Quality) Baltimore-Washington, D.C. Corridor (BWC) Campaign (2011) are compared to surface measurements from EPA's Air Quality Systems (AQS) network. The analysis shows that the CMAQ-HSRL-CH method leads to considerable improvement of CMAQ's predicted PM2.5 concentrations (R2 value increased from 0.37 to 0.63, the root mean square error (RMSE) was reduced from 11.9 to 7.2 μg m-3, and the normalized mean bias (NMB) was lowered from -46.0 to 4.6%). The HSRL-CH method showed statistics (R2=0.75, RMSE=8.6 μgm-3, and NMB=24.0%), which were better than the CMAQ prediction of PM2.5 alone and analogous to CMAQ-HSRL-CH. In addition to mass concentration, HSRL-CH can also provide aerosol chemical composition without specific model simulations. We expect that the HSRL-CH method will be able to make reliable estimates of PM2.5 concentration and chemical composition where HSRL data are available.
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Affiliation(s)
| | | | - Xinyi Ling
- North Carolina State University, Raleigh, NC
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21
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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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22
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Luo H, Astitha M, Hogrefe C, Mathur R, Rao ST. Evaluating trends and seasonality in modeled PM 2.5 concentrations using empirical mode decomposition. ATMOSPHERIC CHEMISTRY AND PHYSICS 2020; 20:13801-13815. [PMID: 33365052 PMCID: PMC7751620] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Regional-scale air quality models are being used for studying the sources, composition, transport, transformation, and deposition of fine particulate matter (PM2.5). The availability of decadal air quality simulations provides a unique opportunity to explore sophisticated model evaluation techniques rather than relying solely on traditional operational evaluations. In this study, we propose a new approach for process-based model evaluation of speciated PM2.5 using improved complete ensemble empirical mode decomposition with adaptive noise (improved CEEMDAN) to assess how well version 5.0.2 of the coupled Weather Research and Forecasting model-Community Multiscale Air Quality model (WRF-CMAQ) simulates the time-dependent long-term trend and cyclical variations in daily average PM2.5 and its species, including sulfate (SO4), nitrate (NO3), ammonium (NH4), chloride (Cl), organic carbon (OC), and elemental carbon (EC). The utility of the proposed approach for model evaluation is demonstrated using PM2.5 data at three monitoring locations. At these locations, the model is generally more capable of simulating the rate of change in the long-term trend component than its absolute magnitude. Amplitudes of the sub-seasonal and annual cycles of total PM2.5, SO4, and OC are well reproduced. However, the time-dependent phase difference in the annual cycles for total PM2.5, OC, and EC reveals a phase shift of up to half a year, indicating the need for proper temporal allocation of emissions and for updating the treatment of organic aerosols compared to the model version used for this set of simulations. Evaluation of sub-seasonal and interannual variations indicates that CMAQ is more capable of replicating the sub-seasonal cycles than interannual variations in magnitude and phase.
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Affiliation(s)
- Huiying Luo
- University of Connecticut, Department of Civil and Environmental Engineering, Storrs-Mansfield, CT, USA
| | - Marina Astitha
- University of Connecticut, Department of Civil and Environmental Engineering, Storrs-Mansfield, CT, USA
| | - Christian Hogrefe
- U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Rohit Mathur
- U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - S. Trivikrama Rao
- University of Connecticut, Department of Civil and Environmental Engineering, Storrs-Mansfield, CT, USA
- North Carolina State University, Raleigh, NC, USA
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23
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Baker KR, Nguyen TKV, Sareen N, Henderson BH. Meteorological and Air Quality Modeling for Hawaii, Puerto Rico, and Virgin Islands. ATMOSPHERIC ENVIRONMENT (OXFORD, ENGLAND : 1994) 2020; 234:117543-11753. [PMID: 32601520 PMCID: PMC7322826 DOI: 10.1016/j.atmosenv.2020.117543] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
A photochemical model platform for Hawaii, Puerto Rico, and Virgin Islands predicting O3, PM2.5, and regional haze would be useful to support assessments relevant for the National Ambient Air Quality Standards (NAAQS), Regional Haze Rule, and the Prevention of Significant Deterioration (PSD) program. These areas have not traditionally been modeled with photochemical transport models, but a reasonable representation of meteorology, emissions (natural and anthropogenic), chemistry, and deposition could support air quality management decisions in these areas. Here, a prognostic meteorological model (Weather Research and Forecasting) and photochemical transport (Community Multiscale Air Quality) model were applied for the entire year of 2016 at 27, 9, and 3 km grid resolution for areas covering the Hawaiian Islands and Puerto Rico/Virgin Islands. Model predictions were compared against surface and upper air meteorological and chemical measurements available in both areas. The vertical gradient of temperature, humidity, and winds in the troposphere was well represented. Surface layer meteorological model performance was spatially variable, but temperature tended to be underestimated in Hawaii. Chemically speciated daily average PM2.5 was generally well characterized by the modeling system at urban and rural monitors in Hawaii and Puerto Rico/Virgin Islands. Model performance was notably impacted by the wildfire emission methodology. Model performance was mixed for hourly SO2, NO2, PM2.5, and CO and was often related to how well local emissions sources were characterized. SO2 predictions were much lower than measurements at monitors near active volcanos on Hawaii, which was expected since volcanic emissions were not included in these model simulations. Further research is needed to assess emission inventory representation of these areas and how microscale meteorology influenced by the complex land-water and terrain interfaces impacts higher time resolution performance.
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Affiliation(s)
- K R Baker
- U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - T K V Nguyen
- U.S. Environmental Protection Agency, San Francisco, CA, USA
| | - N Sareen
- U.S. Environmental Protection Agency, New York, NY, USA
| | - B H Henderson
- U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
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