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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. Geosci Model Dev 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] [What about the content of this article? (0)] [Affiliation(s)] [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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Zhang Y, Bash JO, Roselle SJ, Shatas A, Repinsky A, Mathur R, Hogrefe C, Piziali J, Jacobs T, Gilliland A. Unexpected air quality impacts from implementation of green infrastructure in urban environments: A Kansas City case study. Sci Total Environ 2020; 744:140960. [PMID: 32711327 PMCID: PMC7802588 DOI: 10.1016/j.scitotenv.2020.140960] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/31/2020] [Revised: 07/09/2020] [Accepted: 07/12/2020] [Indexed: 06/11/2023]
Abstract
Green infrastructure (GI) implementation can benefit an urban environment by reducing the impacts of urban stormwater on aquatic ecosystems and human health. However, few studies have systematically analyzed the biophysical effects on regional meteorology and air quality that are triggered by changes in the urban vegetative coverage. In this study we use a state-of-the-art high-resolution air quality model to simulate the effects of a hypothetically feasible vegetation-focused GI implementation scenario in Kansas City, MO/KS on regional meteorology and air quality. Full year simulations are conducted for both the base case and GI land use scenarios using two different land surface models (LSMs) schemes inside the meteorological model. While the magnitudes of the changes in air quality due to the GI implementation differ using the two LSMs, the model outputs consistently showed increases in summertime PM2.5 (1.1 μg m-3, approximately 10% increase using NOAH LSM), which occurred mostly during the night and arose from the primary components, due to the cooler surface temperatures and the decreased planetary boundary layer height (PBLH). Both the maximum daily 8-hour average ozone and 1 h daily maximum O3 during summertime, decreased over the downtown areas (maximum decreases of 0.9 and 1.4 ppbv respectively). The largest ozone decreases were simulated to happen during the night, mainly caused by the titration effect of increased NOx concentration from the lower PBLH. These results highlight the region-specific non-linear process feedback from GI on regional air quality, and further demonstrate the need for comprehensive coupled meteorological-air quality modeling systems and necessity of accurate land surface model for studying these impacts.
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Affiliation(s)
- Yuqiang Zhang
- Oak Ridge Institute for Science and Education (ORISE) Fellowship Participant at U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, United States of America; Nicholas School of the Environment, Duke University, Durham, NC 27708, United States of America.
| | - Jesse O Bash
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, United States of America
| | - Shawn J Roselle
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, United States of America
| | - Angie Shatas
- Outreach Information Division, Office of Air Quality Planning and Standards, Office of Air and Radiation, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, United States of America
| | - Andrea Repinsky
- Research Services Department, Mid-America Regional Council, Kansas City, MO 64105, United States of America
| | - Rohit Mathur
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, United States of America
| | - Christian Hogrefe
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, United States of America
| | - Jamie Piziali
- Water Permits Division, Office of Wastewater Management, Office of Water, U.S. Environmental Protection Agency, Washington, DC 20460, United States of America
| | - Tom Jacobs
- Transportation and Environment, Mid-America Regional Council, Kansas City, MO 64105, United States of America
| | - Alice Gilliland
- National Risk Management Research Laboratory, Office of Research and Development, U.S. Environmental Protection Agency, Cincinnati, OH 45268, United States of America
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Kang D, Pickering KE, Allen DJ, Foley KM, Wong DC, Mathur R, Roselle SJ. Simulating lightning NO production in CMAQv5.2: evolution of scientific updates. Geosci Model Dev 2019; 12:3071-3083. [PMID: 32206207 PMCID: PMC7087390 DOI: 10.5194/gmd-12-3071-2019] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
This work describes the lightning nitric oxide (LNO) production schemes in the Community Multiscale Air Quality (CMAQ) model. We first document the existing LNO production scheme and vertical distribution algorithm. We then describe updates that were made to the scheme originally based on monthly National Lightning Detection Network (mNLDN) observations. The updated scheme uses hourly NLDN (hNLDN) observations. These NLDN-based schemes are good for retrospective model applications when historical lightning data are available. For applications when observed data are not available (i.e., air quality forecasts and climate studies that assume similar climate conditions), we have developed a scheme that is based on linear and log-linear parameters derived from regression of multiyear historical NLDN (pNLDN) observations and meteorological model simulations. Preliminary assessment for total column LNO production reveals that the mNLDN scheme overestimates LNO by over 40% during summer months compared with the updated hNLDN scheme that reflects the observed lightning activity more faithfully in time and space. The pNLDN performance varies with year, but it generally produced LNO columns that are comparable to hNLDN and mNLDN, and in most cases it outperformed mNLDN. Thus, when no observed lightning data are available, pNLDN can provide reasonable estimates of LNO emissions over time and space for this important natural NO source that influences air quality regulations.
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Affiliation(s)
- Daiwen Kang
- National Exposure Research Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Kenneth E Pickering
- Department of Atmospheric and Oceanic Science, University of Maryland, College Park, MD, USA
| | - Dale J Allen
- Department of Atmospheric and Oceanic Science, University of Maryland, College Park, MD, USA
| | - Kristen M Foley
- National Exposure Research Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - David C Wong
- National Exposure Research Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Rohit Mathur
- National Exposure Research Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Shawn J Roselle
- National Exposure Research Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
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Kang D, Foley KM, Mathur R, Roselle SJ, Pickering KE, Allen DJ. Simulating lightning NO production in CMAQv5.2: performance evaluations. Geosci Model Dev 2019; 12:4409-4424. [PMID: 31844504 PMCID: PMC6913039 DOI: 10.5194/gmd-12-4409-2019] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2023]
Abstract
This study assesses the impact of the lightning nitric oxide (LNO) production schemes in the Community Multiscale Air Quality (CMAQ) model on ground-level air quality as well as aloft atmospheric chemistry through detailed evaluation of model predictions of nitrogen oxides (NO x ) and ozone (O3) with corresponding observations for the US. For ground-level evaluations, hourly O3 and NO x values from the U.S. EPA Air Quality System (AQS) monitoring network are used to assess the impact of different LNO schemes on model prediction of these species in time and space. Vertical evaluations are performed using ozonesonde and P-3B aircraft measurements during the Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) campaign conducted in the Baltimore- Washington region during July 2011. The impact on wet deposition of nitrate is assessed using measurements from the National Atmospheric Deposition Program's National Trends Network (NADP NTN). Compared with the Base model (without LNO), the impact of LNO on surface O3 varies from region to region depending on the Base model conditions. Overall statistics suggest that for regions where surface O3 mixing ratios are already overestimated, the incorporation of additional NO from lightning generally increased model overestimation of mean daily maximum 8 h (DM8HR) O3 by 1-2 ppb. In regions where surface O3 is underestimated by the Base model, LNO can significantly reduce the underestimation and bring model predictions close to observations. Analysis of vertical profiles reveals that LNO can significantly improve the vertical structure of modeled O3 distributions by reducing underestimation aloft and to a lesser degree decreasing overestimation near the surface. Since the Base model underestimates the wet deposition of nitrate in most regions across the modeling domain with the exception of the Pacific Coast, the inclusion of LNO leads to reduction in biases and errors and an increase in correlation coefficients at almost all the NADP NTN sites. Among the three LNO schemes described in Kang et al. (2019), the hNLDN scheme, which is implemented using hourly observed lightning flash data from National Lightning Detection Network (NLDN), performs best for comparisons with ground-level values, vertical profiles, and wet deposition of nitrate; the mNLDN scheme (the monthly NLDN-based scheme) performed slightly better. However, when observed lightning flash data are not available, the linear regression-based parameterization scheme, pNLDN, provides an improved estimate for nitrate wet deposition compared to the base simulation that does not include LNO.
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Affiliation(s)
- Daiwen Kang
- Center for Environmental Measurement and Modeling, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Kristen M. Foley
- Center for Environmental Measurement and Modeling, 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
| | - Shawn J. Roselle
- Center for Environmental Measurement and Modeling, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Kenneth E. Pickering
- Department of Atmospheric and Oceanic Science, University of Maryland, College Park, MD, USA
| | - Dale J. Allen
- Department of Atmospheric and Oceanic Science, University of Maryland, College Park, MD, USA
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Zhang Y, Mathur R, Bash JO, Hogrefe C, Xing J, Roselle SJ. Long-term trends in total inorganic nitrogen and sulfur deposition in the U.S. from 1990 to 2010. Atmos Chem Phys 2018; 18:9091-9106. [PMID: 30079084 PMCID: PMC6069975 DOI: 10.5194/acp-18-9091-2018] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
Excess deposition (including both wet and dry deposition) of nitrogen and sulfur are detrimental to ecosystems. Recent studies have investigated the spatial patterns and temporal trends of nitrogen and sulfur wet deposition, but few studies have focused on dry deposition due to the scarcity of dry deposition measurements. Here, we use long-term model simulations from the coupled WRF-CMAQ model covering the period from 1990 to 2010 to study changes in spatial distribution as well as temporal trends in total (TDEP), wet (WDEP) and dry deposition (DDEP) of total inorganic nitrogen (TIN) and sulfur (TSO4). We first evaluate the model's performance in simulating WDEP over the U.S. by comparing the model results with observational data from the U.S. National Atmospheric Deposition Program. The coupled model generally underestimates the WDEP of both TIN (including both the oxidized nitrogen deposition-TNO3, and the reduced nitrogen deposition-NHX) and TSO4, with better performance in the eastern U.S. than the western U.S. TDEP of both TIN and TSO4 show significant decreases over the U.S., especially in the east due to the large emission reductions that occurred in that region. The decreasing trends of TIN TDEP are caused by decrease of TNO3, and the increasing trends of TIN deposition over the Great Plains and Tropical Wet Forests regions are caused by increases in NH3 emissions although it should be noted that these increasing trends are not significant. TIN WDEP shows decreasing trends throughout the U.S., except for the Marine West Coast Forest region. TIN DDEP shows significant decreasing trends in the region of Eastern Temperate Forests, Northern Forests, Mediterranean California and Marine West Coast Forest, and significant increasing trends in the region of Tropical Wet Forests, Great Plains and Southern Semi-arid Highlands. For the other three regions (North American Deserts, Temperate Sierras and Northwestern Forested Mountains), the decreasing or increasing trends were not significant. Both the WDEP and DDEP of TSOx have decreases across the U.S., with a larger decreasing trend in the DDEP than that in the WDEP. Across the U.S. during the 1990-2010 period, DDEP of TIN accounted for 58-65% of TDEP of TIN. TDEP of TIN over the U.S. was dominated by deposition of TNO3 during the first decade, which then shifts to reduced nitrogen (NHX) dominance after 2003 resulting from combination of NOx emission reductions and NH3 emission increases. The sulfur DDEP is usually higher than the sulfur WDEP until recent years, as the sulfur DDEP has a larger decreasing trend than WDEP.
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Affiliation(s)
- Yuqiang Zhang
- Oak Ridge Institute for Science and Education (ORISE) Fellowship Participant at US Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Rohit Mathur
- US Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Jesse O. Bash
- US Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Christian Hogrefe
- US Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Jia Xing
- School of Environment, Tsinghua University, Beijing, 100084, China
| | - Shawn J. Roselle
- US Environmental Protection Agency, Research Triangle Park, NC 27711, USA
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Zhang Y, Mathur R, Bash JO, Hogrefe C, Xing J, Roselle SJ. Long-term trends in total inorganic nitrogen and sulfur deposition in the U.S. from 1990 to 2010. Atmos Chem Phys 2018; 18:9091-9106. [PMID: 30079084 DOI: 10.5194/acp-18-90912018] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
Excess deposition (including both wet and dry deposition) of nitrogen and sulfur are detrimental to ecosystems. Recent studies have investigated the spatial patterns and temporal trends of nitrogen and sulfur wet deposition, but few studies have focused on dry deposition due to the scarcity of dry deposition measurements. Here, we use long-term model simulations from the coupled WRF-CMAQ model covering the period from 1990 to 2010 to study changes in spatial distribution as well as temporal trends in total (TDEP), wet (WDEP) and dry deposition (DDEP) of total inorganic nitrogen (TIN) and sulfur (TSO4). We first evaluate the model's performance in simulating WDEP over the U.S. by comparing the model results with observational data from the U.S. National Atmospheric Deposition Program. The coupled model generally underestimates the WDEP of both TIN (including both the oxidized nitrogen deposition-TNO3, and the reduced nitrogen deposition-NHX) and TSO4, with better performance in the eastern U.S. than the western U.S. TDEP of both TIN and TSO4 show significant decreases over the U.S., especially in the east due to the large emission reductions that occurred in that region. The decreasing trends of TIN TDEP are caused by decrease of TNO3, and the increasing trends of TIN deposition over the Great Plains and Tropical Wet Forests regions are caused by increases in NH3 emissions although it should be noted that these increasing trends are not significant. TIN WDEP shows decreasing trends throughout the U.S., except for the Marine West Coast Forest region. TIN DDEP shows significant decreasing trends in the region of Eastern Temperate Forests, Northern Forests, Mediterranean California and Marine West Coast Forest, and significant increasing trends in the region of Tropical Wet Forests, Great Plains and Southern Semi-arid Highlands. For the other three regions (North American Deserts, Temperate Sierras and Northwestern Forested Mountains), the decreasing or increasing trends were not significant. Both the WDEP and DDEP of TSOx have decreases across the U.S., with a larger decreasing trend in the DDEP than that in the WDEP. Across the U.S. during the 1990-2010 period, DDEP of TIN accounted for 58-65% of TDEP of TIN. TDEP of TIN over the U.S. was dominated by deposition of TNO3 during the first decade, which then shifts to reduced nitrogen (NHX) dominance after 2003 resulting from combination of NOx emission reductions and NH3 emission increases. The sulfur DDEP is usually higher than the sulfur WDEP until recent years, as the sulfur DDEP has a larger decreasing trend than WDEP.
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Affiliation(s)
- Yuqiang Zhang
- Oak Ridge Institute for Science and Education (ORISE) Fellowship Participant at US Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Rohit Mathur
- US Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Jesse O Bash
- US Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Christian Hogrefe
- US Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Jia Xing
- School of Environment, Tsinghua University, Beijing, 100084, China
| | - Shawn J Roselle
- US Environmental Protection Agency, Research Triangle Park, NC 27711, USA
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Zhang Y, West JJ, Mathur R, Xing J, Hogrefe C, Roselle SJ, Bash JO, Pleim JE, Gan CM, Wong DC. Long-term trends in the ambient PM 2.5- and O 3-related mortality burdens in the United States under emission reductions from 1990 to 2010. Atmos Chem Phys 2018; 18:15003-15016. [PMID: 30930942 PMCID: PMC6436631 DOI: 10.5194/acp-18-15003-2018] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
Concentrations of both fine particulate matter (PM2.5) and ozone (O3) in the United States (US) have decreased significantly since 1990, mainly because of air quality regulations. Exposure to these air pollutants is associated with premature death. Here we quantify the annual mortality burdens from PM2.5 and O3 in the US from 1990 to 2010, estimate trends and inter-annual variability, and evaluate the contributions to those trends from changes in pollutant concentrations, population, and baseline mortality rates. We use a fine-resolution (36 km) self-consistent 21-year simulation of air pollutant concentrations in the US from 1990 to 2010, a health impact function, and annual county-level population and baseline mortality rate estimates. From 1990 to 2010, the modeled population-weighted annual PM2.5 decreased by 39 %, and summertime (April to September) 1 h average daily maximum O3 decreased by 9 % from 1990 to 2010. The PM2.5-related mortality burden from ischemic heart disease, chronic obstructive pulmonary disease, lung cancer, and stroke steadily decreased by 54% from 123 700 deaths year-1 (95% confidence interval, 70 800-178 100) in 1990 to 58 600 deaths year-1 (24 900-98 500) in 2010. The PM2.5-related mortality burden would have decreased by only 24% from 1990 to 2010 if the PM2.5 concentrations had stayed at the 1990 level, due to decreases in baseline mortality rates for major diseases affected by PM2.5. The mortality burden associated with O3 from chronic respiratory disease increased by 13% from 10 900 deaths year-1 (3700-17 500) in 1990 to 12 300 deaths year-1 (4100-19 800) in 2010, mainly caused by increases in the baseline mortality rates and population, despite decreases in O3 concentration. The O3-related mortality burden would have increased by 55% from 1990 to 2010 if the O3 concentrations had stayed at the 1990 level. The detrended annual O3 mortality burden has larger inter-annual variability (coefficient of variation of 12%) than the PM2.5-related burden (4%), mainly from the inter-annual variation of O3 concentration. We conclude that air quality improvements have significantly decreased the mortality burden, avoiding roughly 35 800 (38%) PM2.5-related deaths and 4600 (27%) O3-related deaths in 2010, compared to the case if air quality had stayed at 1990 levels (at 2010 baseline mortality rates and population).
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Affiliation(s)
- Yuqiang Zhang
- Oak Ridge Institute for Science and Education (ORISE) Fellowship Participant at US Environmental Protection Agency, Research Triangle Park, NC 27711, USA
- now at: Nicholas School of the Environment, Duke University, Durham, NC 27710, USA
| | - J. Jason West
- Department of Environmental Sciences and Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Rohit Mathur
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Jia Xing
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Christian Hogrefe
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Shawn J. Roselle
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Jesse O. Bash
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Jonathan E. Pleim
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Chuen-Meei Gan
- CSC Government Solutions LLC, A CSRA Company, Research Triangle Park, NC 27709, USA
| | - David C. Wong
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC 27711, USA
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Appel KW, Napelenok SL, Foley KM, Pye HOT, Hogrefe C, Luecken DJ, Bash JO, Roselle SJ, Pleim JE, Foroutan H, Hutzell WT, Pouliot GA, Sarwar G, Fahey KM, Gantt B, Gilliam RC, Heath NK, Kang D, Mathur R, Schwede DB, Spero TL, Wong DC, Young JO. Description and evaluation of the Community Multiscale Air Quality (CMAQ) modeling system version 5.1. Geosci Model Dev 2017. [PMID: 30147852 DOI: 10.5194/gmd-1703-2017] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
The Community Multiscale Air Quality (CMAQ) model is a comprehensive multipollutant air quality modeling system developed and maintained by the US Environmental Protection Agency's (EPA) Office of Research and Development (ORD). Recently, version 5.1 of the CMAQ model (v5.1) was released to the public, incorporating a large number of science updates and extended capabilities over the previous release version of the model (v5.0.2). These updates include the following: improvements in the meteorological calculations in both CMAQ and the Weather Research and Forecast (WRF) model used to provide meteorological fields to CMAQ, updates to the gas and aerosol chemistry, revisions to the calculations of clouds and photolysis, and improvements to the dry and wet deposition in the model. Sensitivity simulations isolating several of the major updates to the modeling system show that changes to the meteorological calculations result in enhanced afternoon and early evening mixing in the model, periods when the model historically underestimates mixing. This enhanced mixing results in higher ozone (O3) mixing ratios on average due to reduced NO titration, and lower fine particulate matter (PM2.5) concentrations due to greater dilution of primary pollutants (e.g., elemental and organic carbon). Updates to the clouds and photolysis calculations greatly improve consistency between the WRF and CMAQ models and result in generally higher O3 mixing ratios, primarily due to reduced cloudiness and attenuation of photolysis in the model. Updates to the aerosol chemistry result in higher secondary organic aerosol (SOA) concentrations in the summer, thereby reducing summertime PM2.5 bias (PM2.5 is typically underestimated by CMAQ in the summer), while updates to the gas chemistry result in slightly higher O3 and PM2.5 on average in January and July. Overall, the seasonal variation in simulated PM2.5 generally improves in CMAQv5.1 (when considering all model updates), as simulated PM2.5 concentrations decrease in the winter (when PM2.5 is generally overestimated by CMAQ) and increase in the summer (when PM2.5 is generally underestimated by CMAQ). Ozone mixing ratios are higher on average with v5.1 vs. v5.0.2, resulting in higher O3 mean bias, as O3 tends to be overestimated by CMAQ throughout most of the year (especially at locations where the observed O3 is low); however, O3 correlation is largely improved with v5.1. Sensitivity simulations for several hypothetical emission reduction scenarios show that v5.1 tends to be slightly more responsive to reductions in NO x (NO + NO2), VOC and SO x (SO2 + SO4) emissions than v5.0.2, representing an improvement as previous studies have shown CMAQ to underestimate the observed reduction in O3 due to large, widespread reductions in observed emissions.
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Affiliation(s)
- K Wyat Appel
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Sergey L Napelenok
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Kristen M Foley
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Havala O T Pye
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Christian Hogrefe
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Deborah J Luecken
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Jesse O Bash
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Shawn J Roselle
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Jonathan E Pleim
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Hosein Foroutan
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - William T Hutzell
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - George A Pouliot
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Golam Sarwar
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Kathleen M Fahey
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Brett Gantt
- Air Quality Analysis Division, Office of Air Quality Planning and Standards, Office of Air and Radiation, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Robert C Gilliam
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Nicholas K Heath
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Daiwen Kang
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Rohit Mathur
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Donna B Schwede
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Tanya L Spero
- Systems Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - David C Wong
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Jeffrey O Young
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
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Abstract
This study investigates the effect of initial conditions (IC) for pollutant concentrations in the atmosphere and soil on simulated air quality for two continental-scale Community Multiscale Air Quality (CMAQ) model applications. One of these applications was performed for springtime and the second for summertime. Results show that a spin-up period of ten days commonly used in regional-scale applications may not be sufficient to reduce the effects of initial conditions to less than 1% of seasonally-averaged surface ozone concentrations everywhere while 20 days were found to be sufficient for the entire domain for the spring case and almost the entire domain for the summer case. For the summer case, differences were found to persist longer aloft due to circulation of air masses and even a spin-up period of 30 days was not sufficient to reduce the effects of ICs to less than 1% of seasonally-averaged layer 34 ozone concentrations over the southwestern portion of the modeling domain. Analysis of the effect of soil initial conditions for the CMAQ bidirectional NH3 exchange model shows that during springtime they can have an important effect on simulated inorganic aerosols concentrations for time periods of one month or longer. The effects are less pronounced during other seasons. The results, while specific to the modeling domain and time periods simulated here, suggest that modeling protocols need to be scrutinized for a given application and that it cannot be assumed that commonly-used spin-up periods are necessarily sufficient to reduce the effects of initial conditions on model results to an acceptable level. What constitutes an acceptable level of difference cannot be generalized and will depend on the particular application, time period and species of interest. Moreover, as the application of air quality models is being expanded to cover larger geographical domains and as these models are increasingly being coupled with other modeling systems to better represent air-surface-water exchanges, the effects of model initialization in such applications needs to be studied in future work.
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Affiliation(s)
- Christian Hogrefe
- Computational Exposure Division, National Exposure Research Laboratory, U.S. Environmental Protection Agency (EPA), Research Triangle Park, NC 27711, USA
| | - Shawn J Roselle
- Computational Exposure Division, National Exposure Research Laboratory, U.S. Environmental Protection Agency (EPA), Research Triangle Park, NC 27711, USA
| | - Jesse O Bash
- Computational Exposure Division, National Exposure Research Laboratory, U.S. Environmental Protection Agency (EPA), Research Triangle Park, NC 27711, USA
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10
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Appel KW, Napelenok SL, Foley KM, Pye HOT, Hogrefe C, Luecken DJ, Bash JO, Roselle SJ, Pleim JE, Foroutan H, Hutzell WT, Pouliot GA, Sarwar G, Fahey KM, Gantt B, Gilliam RC, Heath NK, Kang D, Mathur R, Schwede DB, Spero TL, Wong DC, Young JO. Description and evaluation of the Community Multiscale Air Quality (CMAQ) modeling system version 5.1. Geosci Model Dev 2017; 10:1703-1732. [PMID: 30147852 DOI: 10.5194/gmd-2016-226] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2023]
Abstract
The Community Multiscale Air Quality (CMAQ) model is a comprehensive multipollutant air quality modeling system developed and maintained by the US Environmental Protection Agency's (EPA) Office of Research and Development (ORD). Recently, version 5.1 of the CMAQ model (v5.1) was released to the public, incorporating a large number of science updates and extended capabilities over the previous release version of the model (v5.0.2). These updates include the following: improvements in the meteorological calculations in both CMAQ and the Weather Research and Forecast (WRF) model used to provide meteorological fields to CMAQ, updates to the gas and aerosol chemistry, revisions to the calculations of clouds and photolysis, and improvements to the dry and wet deposition in the model. Sensitivity simulations isolating several of the major updates to the modeling system show that changes to the meteorological calculations result in enhanced afternoon and early evening mixing in the model, periods when the model historically underestimates mixing. This enhanced mixing results in higher ozone (O3) mixing ratios on average due to reduced NO titration, and lower fine particulate matter (PM2.5) concentrations due to greater dilution of primary pollutants (e.g., elemental and organic carbon). Updates to the clouds and photolysis calculations greatly improve consistency between the WRF and CMAQ models and result in generally higher O3 mixing ratios, primarily due to reduced cloudiness and attenuation of photolysis in the model. Updates to the aerosol chemistry result in higher secondary organic aerosol (SOA) concentrations in the summer, thereby reducing summertime PM2.5 bias (PM2.5 is typically underestimated by CMAQ in the summer), while updates to the gas chemistry result in slightly higher O3 and PM2.5 on average in January and July. Overall, the seasonal variation in simulated PM2.5 generally improves in CMAQv5.1 (when considering all model updates), as simulated PM2.5 concentrations decrease in the winter (when PM2.5 is generally overestimated by CMAQ) and increase in the summer (when PM2.5 is generally underestimated by CMAQ). Ozone mixing ratios are higher on average with v5.1 vs. v5.0.2, resulting in higher O3 mean bias, as O3 tends to be overestimated by CMAQ throughout most of the year (especially at locations where the observed O3 is low); however, O3 correlation is largely improved with v5.1. Sensitivity simulations for several hypothetical emission reduction scenarios show that v5.1 tends to be slightly more responsive to reductions in NO x (NO + NO2), VOC and SO x (SO2 + SO4) emissions than v5.0.2, representing an improvement as previous studies have shown CMAQ to underestimate the observed reduction in O3 due to large, widespread reductions in observed emissions.
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Affiliation(s)
- K Wyat Appel
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Sergey L Napelenok
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Kristen M Foley
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Havala O T Pye
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Christian Hogrefe
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Deborah J Luecken
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Jesse O Bash
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Shawn J Roselle
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Jonathan E Pleim
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Hosein Foroutan
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - William T Hutzell
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - George A Pouliot
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Golam Sarwar
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Kathleen M Fahey
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Brett Gantt
- Air Quality Analysis Division, Office of Air Quality Planning and Standards, Office of Air and Radiation, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Robert C Gilliam
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Nicholas K Heath
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Daiwen Kang
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Rohit Mathur
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Donna B Schwede
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Tanya L Spero
- Systems Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - David C Wong
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Jeffrey O Young
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
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11
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Appel KW, Napelenok SL, Foley KM, Pye HOT, Hogrefe C, Luecken DJ, Bash JO, Roselle SJ, Pleim JE, Foroutan H, Hutzell WT, Pouliot GA, Sarwar G, Fahey KM, Gantt B, Gilliam RC, Heath NK, Kang D, Mathur R, Schwede DB, Spero TL, Wong DC, Young JO. Description and evaluation of the Community Multiscale Air Quality (CMAQ) modeling system version 5.1. Geosci Model Dev 2017; 10:1703-1732. [PMID: 30147852 DOI: 10.5194/gmd-3-205-2010] [Citation(s) in RCA: 136] [Impact Index Per Article: 19.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
The Community Multiscale Air Quality (CMAQ) model is a comprehensive multipollutant air quality modeling system developed and maintained by the US Environmental Protection Agency's (EPA) Office of Research and Development (ORD). Recently, version 5.1 of the CMAQ model (v5.1) was released to the public, incorporating a large number of science updates and extended capabilities over the previous release version of the model (v5.0.2). These updates include the following: improvements in the meteorological calculations in both CMAQ and the Weather Research and Forecast (WRF) model used to provide meteorological fields to CMAQ, updates to the gas and aerosol chemistry, revisions to the calculations of clouds and photolysis, and improvements to the dry and wet deposition in the model. Sensitivity simulations isolating several of the major updates to the modeling system show that changes to the meteorological calculations result in enhanced afternoon and early evening mixing in the model, periods when the model historically underestimates mixing. This enhanced mixing results in higher ozone (O3) mixing ratios on average due to reduced NO titration, and lower fine particulate matter (PM2.5) concentrations due to greater dilution of primary pollutants (e.g., elemental and organic carbon). Updates to the clouds and photolysis calculations greatly improve consistency between the WRF and CMAQ models and result in generally higher O3 mixing ratios, primarily due to reduced cloudiness and attenuation of photolysis in the model. Updates to the aerosol chemistry result in higher secondary organic aerosol (SOA) concentrations in the summer, thereby reducing summertime PM2.5 bias (PM2.5 is typically underestimated by CMAQ in the summer), while updates to the gas chemistry result in slightly higher O3 and PM2.5 on average in January and July. Overall, the seasonal variation in simulated PM2.5 generally improves in CMAQv5.1 (when considering all model updates), as simulated PM2.5 concentrations decrease in the winter (when PM2.5 is generally overestimated by CMAQ) and increase in the summer (when PM2.5 is generally underestimated by CMAQ). Ozone mixing ratios are higher on average with v5.1 vs. v5.0.2, resulting in higher O3 mean bias, as O3 tends to be overestimated by CMAQ throughout most of the year (especially at locations where the observed O3 is low); however, O3 correlation is largely improved with v5.1. Sensitivity simulations for several hypothetical emission reduction scenarios show that v5.1 tends to be slightly more responsive to reductions in NO x (NO + NO2), VOC and SO x (SO2 + SO4) emissions than v5.0.2, representing an improvement as previous studies have shown CMAQ to underestimate the observed reduction in O3 due to large, widespread reductions in observed emissions.
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Affiliation(s)
- K Wyat Appel
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Sergey L Napelenok
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Kristen M Foley
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Havala O T Pye
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Christian Hogrefe
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Deborah J Luecken
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Jesse O Bash
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Shawn J Roselle
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Jonathan E Pleim
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Hosein Foroutan
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - William T Hutzell
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - George A Pouliot
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Golam Sarwar
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Kathleen M Fahey
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Brett Gantt
- Air Quality Analysis Division, Office of Air Quality Planning and Standards, Office of Air and Radiation, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Robert C Gilliam
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Nicholas K Heath
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Daiwen Kang
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Rohit Mathur
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Donna B Schwede
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Tanya L Spero
- Systems Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - David C Wong
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Jeffrey O Young
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
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12
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Appel KW, Napelenok SL, Foley KM, Pye HOT, Hogrefe C, Luecken DJ, Bash JO, Roselle SJ, Pleim JE, Foroutan H, Hutzell WT, Pouliot GA, Sarwar G, Fahey KM, Gantt B, Gilliam RC, Heath NK, Kang D, Mathur R, Schwede DB, Spero TL, Wong DC, Young JO. Description and evaluation of the Community Multiscale Air Quality (CMAQ) modeling system version 5.1. Geosci Model Dev 2017; 10:1703-1732. [PMID: 30147852 PMCID: PMC6104654 DOI: 10.5194/gmd-10-1703-2017] [Citation(s) in RCA: 97] [Impact Index Per Article: 13.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
The Community Multiscale Air Quality (CMAQ) model is a comprehensive multipollutant air quality modeling system developed and maintained by the US Environmental Protection Agency's (EPA) Office of Research and Development (ORD). Recently, version 5.1 of the CMAQ model (v5.1) was released to the public, incorporating a large number of science updates and extended capabilities over the previous release version of the model (v5.0.2). These updates include the following: improvements in the meteorological calculations in both CMAQ and the Weather Research and Forecast (WRF) model used to provide meteorological fields to CMAQ, updates to the gas and aerosol chemistry, revisions to the calculations of clouds and photolysis, and improvements to the dry and wet deposition in the model. Sensitivity simulations isolating several of the major updates to the modeling system show that changes to the meteorological calculations result in enhanced afternoon and early evening mixing in the model, periods when the model historically underestimates mixing. This enhanced mixing results in higher ozone (O3) mixing ratios on average due to reduced NO titration, and lower fine particulate matter (PM2.5) concentrations due to greater dilution of primary pollutants (e.g., elemental and organic carbon). Updates to the clouds and photolysis calculations greatly improve consistency between the WRF and CMAQ models and result in generally higher O3 mixing ratios, primarily due to reduced cloudiness and attenuation of photolysis in the model. Updates to the aerosol chemistry result in higher secondary organic aerosol (SOA) concentrations in the summer, thereby reducing summertime PM2.5 bias (PM2.5 is typically underestimated by CMAQ in the summer), while updates to the gas chemistry result in slightly higher O3 and PM2.5 on average in January and July. Overall, the seasonal variation in simulated PM2.5 generally improves in CMAQv5.1 (when considering all model updates), as simulated PM2.5 concentrations decrease in the winter (when PM2.5 is generally overestimated by CMAQ) and increase in the summer (when PM2.5 is generally underestimated by CMAQ). Ozone mixing ratios are higher on average with v5.1 vs. v5.0.2, resulting in higher O3 mean bias, as O3 tends to be overestimated by CMAQ throughout most of the year (especially at locations where the observed O3 is low); however, O3 correlation is largely improved with v5.1. Sensitivity simulations for several hypothetical emission reduction scenarios show that v5.1 tends to be slightly more responsive to reductions in NO x (NO + NO2), VOC and SO x (SO2 + SO4) emissions than v5.0.2, representing an improvement as previous studies have shown CMAQ to underestimate the observed reduction in O3 due to large, widespread reductions in observed emissions.
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Affiliation(s)
- K. Wyat Appel
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Sergey L. Napelenok
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Kristen M. Foley
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Havala O. T. Pye
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Christian Hogrefe
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Deborah J. Luecken
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Jesse O. Bash
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Shawn J. Roselle
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Jonathan E. Pleim
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Hosein Foroutan
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - William T. Hutzell
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - George A. Pouliot
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Golam Sarwar
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Kathleen M. Fahey
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Brett Gantt
- Air Quality Analysis Division, Office of Air Quality Planning and Standards, Office of Air and Radiation, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Robert C. Gilliam
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Nicholas K. Heath
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Daiwen Kang
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Rohit Mathur
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Donna B. Schwede
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Tanya L. Spero
- Systems Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - David C. Wong
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Jeffrey O. Young
- Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
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13
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Carlton AG, Turpin BI, Altieri KE, Seitzinger SP, Mathur R, Roselle SJ, Weber RJ. CMAQ model performance enhanced when in-cloud secondary organic aerosol is included: comparisons of organic carbon predictions with measurements. Environ Sci Technol 2008; 42:8798-802. [PMID: 19192800 DOI: 10.1021/es801192n] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Mounting evidence suggests that low-volatility (particle-phase) organic compounds form in the atmosphere through aqueous phase reactions in clouds and aerosols. Although some models have begun including secondary organic aerosol (SOA) formation through cloud processing, validation studies that compare predictions and measurements are needed. In this work, agreement between modeled organic carbon (OC) and aircraft measurements of water soluble OC improved for all 5 of the compared ICARTT NOAA-P3 flights during August when an in-cloud SOA (SOAcld) formation mechanism was added to CMAQ (a regional-scale atmospheric model). The improvement was most dramatic for the August 14th flight, a flight designed specifically to investigate clouds. During this flight the normalized mean bias for layer-averaged OC was reduced from -64 to -15% and correlation (r) improved from 0.5 to 0.6. Underpredictions of OC aloft by atmospheric models may be explained, in part, by this formation mechanism (SOAcld). OC formation aloft contributes to long-range pollution transport and has implications to radiative forcing, regional air quality and climate.
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Affiliation(s)
- Annmarie G Carlton
- Air Resources Laboratory, Atmospheric Sciences Modeling Division, National Oceanic and Atmospheric Administration, 109 TW Alexander Drive, Durham, North Carolina 27711, USA.
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Pour-Biazar A, McNider RT, Roselle SJ, Suggs R, Jedlovec G, Byun DW, Kim S, Lin CJ, Ho TC, Haines S, Dornblaser B, Cameron R. Correcting photolysis rates on the basis of satellite observed clouds. ACTA ACUST UNITED AC 2007. [DOI: 10.1029/2006jd007422] [Citation(s) in RCA: 36] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
| | - Richard T. McNider
- Atmospheric Science Department; University of Alabama; Huntsville Alabama USA
| | - Shawn J. Roselle
- Atmospheric Science Modeling Division, Air Resources Laboratory; NOAA; Research Triangle Park North Carolina USA
| | - Ron Suggs
- NASA Marshall Space Flight Center; Huntsville Alabama USA
| | - Gary Jedlovec
- NASA Marshall Space Flight Center; Huntsville Alabama USA
| | - Daewon W. Byun
- Institute for Multidimensional Air Quality Studies; University of Houston; Houston Texas USA
| | - Soontae Kim
- Institute for Multidimensional Air Quality Studies; University of Houston; Houston Texas USA
| | - C. J. Lin
- Department of Civil Engineering; Lamar University; Beaumont Texas USA
| | - Thomas C. Ho
- Department of Chemical Engineering; Lamar University; Beaumont Texas USA
| | - Stephanie Haines
- Earth System Science Center; University of Alabama; Huntsville Alabama USA
| | | | - Robert Cameron
- Minerals Management Service; Gulf of Mexico OCS Region; New Orleans Louisiana USA
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Mebust MR, Eder BK, Binkowski FS, Roselle SJ. Models‐3 Community Multiscale Air Quality (CMAQ) model aerosol component 2. Model evaluation. ACTA ACUST UNITED AC 2003. [DOI: 10.1029/2001jd001410] [Citation(s) in RCA: 80] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Michelle R. Mebust
- National Exposure Research Laboratory U.S. Environmental Protection Agency Research Triangle Park North Carolina USA
| | - Brian K. Eder
- Air Resources Laboratory National Oceanic and Atmospheric Administration Research Triangle Park North Carolina USA
- On assignment to National Exposure Research Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA
| | - Francis S. Binkowski
- Air Resources Laboratory National Oceanic and Atmospheric Administration Research Triangle Park North Carolina USA
- On assignment to National Exposure Research Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA
| | - Shawn J. Roselle
- Air Resources Laboratory National Oceanic and Atmospheric Administration Research Triangle Park North Carolina USA
- On assignment to National Exposure Research Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA
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Affiliation(s)
- Francis S. Binkowski
- Atmospheric Sciences Modeling Division, Air Resources Laboratory National Oceanic and Atmospheric Administration Research Triangle Park North Carolina USA
- On assignment to National Exposure Research Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA
| | - Shawn J. Roselle
- Atmospheric Sciences Modeling Division, Air Resources Laboratory National Oceanic and Atmospheric Administration Research Triangle Park North Carolina USA
- On assignment to National Exposure Research Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA
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Gilliland AB, Dennis RL, Roselle SJ, Pierce TE, Bender LE. Developing seasonal ammonia emission estimates with an inverse modeling technique. ScientificWorldJournal 2001; 1 Suppl 2:356-62. [PMID: 12805797 PMCID: PMC6083988 DOI: 10.1100/tsw.2001.312] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Significant uncertainty exists in magnitude and variability of ammonia (NH3) emissions, which are needed for air quality modeling of aerosols and deposition of nitrogen compounds. Approximately 85% of NH3 emissions are estimated to come from agricultural nonpoint sources. We suspect a strong seasonal pattern in NH 3 emissions; however, current NH3 emission inventories lack intra-annual variability. Annually averaged NH 3 emissions could significantly affect model-predicted concentrations and wet and dry deposition of nitrogen-containing compounds. We apply a Kalman filter inverse modeling technique to deduce monthly NH3 emissions for the eastern U.S. Final products of this research will include monthly emissions estimates from each season. Results for January and June 1990 are currently available and are presented here. The U.S. Environmental Protection Agency (USEPA) Community Multiscale Air Quality (CMAQ) model and ammonium (NH4+) wet concentration data from the National Atmospheric Deposition Program (NADP) network are used. The inverse modeling technique estimates the emission adjustments that provide optimal modeled results with respect to wet NH4+ concentrations, observational data error, and emission uncertainty. Our results suggest that annual average NH 3 emissions estimates should be decreased by 64% for January 1990 and increased by 25% for June 1990. These results illustrate the strong differences that are anticipated for NH3 emissions.
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Affiliation(s)
- A B Gilliland
- NOAA Air Resources Laboratory, Research Triangle Park, NC 27709, USA.
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Roselle SJ, Schere KL. Modeled response of photochemical oxidants to systematic reductions in anthropogenic volatile organic compound and NOxemissions. ACTA ACUST UNITED AC 1995. [DOI: 10.1029/95jd00505] [Citation(s) in RCA: 28] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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