Comparison of background ozone estimates over the western United States based on two separate model methodologies

Dolwick, Pat; Akhtar, F.; Baker, Kirk R.; Possiel, Norm; Simon, Heather; Tonnesen, Gail

doi:10.1016/j.atmosenv.2015.01.005

Cited by 55 publications

(54 citation statements)

References 30 publications

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“…Many previous studies have used CAMx to simulate ozone with reasonable fidelity [Emery et al, 2012;Dolwick et al, 2015;Koo et al, 2015]. The Anthropogenic Precursor Culpability Assessment (APCA) probing tool in CAMx is used as a means to tag ozone source attribution from twelve source regions and seven source sectors.…”

Section: Methodsmentioning

confidence: 99%

CAMx ozone source attribution in the eastern United States using guidance from observations during DISCOVER‐AQ Maryland

Goldberg

Vinciguerra

Anderson

et al. 2016

Geophysical Research Letters

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A Comprehensive Air‐Quality Model with Extensions (CAMx) version 6.10 simulation was assessed through comparison with data acquired during NASA's 2011 Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER‐AQ) Maryland field campaign. Comparisons for the baseline simulation (Carbon Bond 2005 (CB05) chemistry, Environmental Protection Agency 2011 National Emissions Inventory) show a model overestimate of NOy by +86.2% and an underestimate of HCHO by −28.3%. We present a new model framework (Carbon Bond 6 Revision 2 chemistry (CB6r2), Model of Emissions of Gases and Aerosols from Nature (MEGAN) version 2.1 biogenic emissions, 50% reduction in mobile NOx, enhanced representation of isoprene nitrates) that better matches observations. The new model framework attributes 31.4% more surface ozone in Maryland to electric generating units (EGUs) and 34.6% less ozone to on‐road mobile sources. Surface ozone becomes more NOx limited throughout the eastern United States compared to the baseline simulation. The baseline model therefore likely underestimates the effectiveness of anthropogenic NOx reductions as well as the current contribution of EGUs to surface ozone.

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Section: Methodsmentioning

confidence: 99%

CAMx ozone source attribution in the eastern United States using guidance from observations during DISCOVER‐AQ Maryland

Goldberg

Vinciguerra

Anderson

et al. 2016

Geophysical Research Letters

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“…The PIM was used for this source allocation, which required sensitivities from three additional simulations with emissions and BCs intermediate between the base and background cases. The major unique features of the study are allocating the anthropogenic O 3 increment, rather than the total concentration, and estimating contributions of the anthropogenic components of the BCs, rather than the total BCs (Lefohn et al, 2014;Dolwick et al, 2015;Baker et al, 2015;Nopmongcol et al, 2017).…”

Section: Discussionmentioning

confidence: 99%

“…One approach to apportioning the anthropogenic increment is to remove sources one at a time and determine the change in O 3 from the base case (known as the brute-force or zero-out method) Dolwick et al, 2015). A limitation is that the sum of all the anthropogenic source contributions generally does not equal the anthropogenic increment due to the nonlinear chemistry.…”

Section: Introductionmentioning

confidence: 99%

“…McDonald-Buller et al (2011) review background and baseline O 3 and how the latter can be used to test model results. There are three common definitions of background O 3 : US background, North American background, and natural background, which correspond to elimination of US, North American, or worldwide anthropogenic emissions, respectively Fiore et al, 2014;Dolwick et al, 2015;Nopmongcol et al, 2016). The anthropogenic increment of O 3 is the difference between a base-case simulation with all emissions present and the chosen type of background simulation.…”

Section: Introductionmentioning

confidence: 99%

“…A limitation is that the sum of all the anthropogenic source contributions generally does not equal the anthropogenic increment due to the nonlinear chemistry. Another approach is to add reactive tracers (tagged species) to the base case for the emissions from anthropogenic sources and/or the secondary pollutants formed from the emissions and then use the tracers to estimate the contribution of the sources to the total O 3 concentration (Zhang et al, 2008;Lefohn et al, 2014;Baker et al, 2015;Dolwick et al, 2015;Nopmongcol et al, 2017). Chemistry causes interactions between the sources through direct and indirect effects.…”

Section: Introductionmentioning

confidence: 99%

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Contributions of foreign, domestic and natural emissions to US ozone estimated using the path-integral method in CAMx nested within GEOS-Chem

Dunker¹,

Koo

Yarwood

2017

Atmos. Chem. Phys.

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Abstract. The Goddard Earth Observing System global chemical transport (GEOS-Chem) model was used at 2 • × 2.5 • resolution to simulate ozone formation for a base case representing year 2010 and a natural background case without worldwide anthropogenic emissions. These simulations provided boundary concentrations for base and natural background simulations with the Comprehensive Air Quality Model with Extensions (CAMx) on a North American domain (one-way nested) at 12 km × 12 km resolution over March-September 2010. The predicted maximum daily average 8 h (MDA8) background ozone for the US is largest in the mountainous areas of Colorado, New Mexico, Arizona, and California. The background MDA8 ozone in some of these locations exceeds 60 ppb, when averaged over the 10 days with the largest base-case ozone (T10base average). The background ozone generally becomes both a larger fraction of the base-case ozone in the western US and a smaller fraction in the eastern US when proceeding from spring to summer to the T10base average. The ozone difference between the base and background cases represents the increment to ozone from all anthropogenic sources. The path-integral method was applied to allocate this anthropogenic ozone increment to US anthropogenic emissions, Canadian/Mexican anthropogenic emissions, and the anthropogenic components of the lateral and top boundary concentrations (BCs). Using the T10base average MDA8 ozone, the relative importance of the sources is generally US emissions > anthropogenic lateral BCs > Canadian/Mexican emissions anthropogenic top BCs. Specifically, for 10 US urban areas, the source contributions were 12-53 ppb for US emissions, 3-9 ppb for lateral BCs, 0.2-3 ppb for Canadian/Mexican emissions, and ≤ 0.1 ppb for top BCs. The contributions of the lateral BCs are largest for the higherelevation US sites in the Intermountain West and along the western boundary of the domain. In Denver, for example, the lateral BCs contribute 44 % to the T10base anthropogenic increment. Averaging over the 10 days in the background case with the largest MDA8 ozone (T10bkgd), the contribution from US emissions is reduced to 3-13 ppb at the 10 urban sites, leading to a reduction in the anthropogenic ozone increment. The contribution of the Canadian/Mexican emissions remains about the same (0.3-2 ppb), and the contribution from the lateral BCs increases (5-10 ppb), especially at the lower-elevation urban sites. The net effect at the urban sites is that the relative importance of the anthropogenic lateral BCs is significantly greater for the T10bkgd average than the T10base average. This is also true for rural sites studied, where the anthropogenic lateral BC contribution becomes as large as 68 % of the anthropogenic increment for the T10bkgd average. In addition to the source apportionment, we also used surface and ozonesonde measurements to evaluate GEOS-Chem and CAMx performance.

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Evidence for an increase in the ozone photochemical lifetime in the eastern United States using a regional air quality model

et al. 2015

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Measures to control surface ozone rely on quantifying production attributable to local versus regional (upwind) emissions. Here we simulate the relative contribution of local (i.e., within a particular state) and regional sources of surface ozone in the eastern United States (66–94°W longitude) for July 2002, 2011, and 2018 using the Comprehensive Air‐quality Model with Extensions (CAMx). To determine how emissions and chemistry within the domain affect the production, loss, lifetime, and transport of trace gases, we initialize our model with identical boundary conditions in each simulation. We find that the photochemical lifetime of ozone has increased as emissions have decreased. The contribution of ozone from outside the domain (boundary condition ozone, BCO3) to local surface mixing ratios increases in an absolute sense by 1–2 ppbv between 2002 and 2018 due to the longer lifetime of ozone. The photochemical lifetime of ozone lengthens because the two primary gas phase sinks for odd oxygen (Ox ≈ NO2 + O3)—attack by hydroperoxyl radicals (HO2) on ozone and formation of nitrate—weaken with decreasing pollutant emissions. The relative role of BCO3 will also increase. For example, BCO3 represents 34.5%, 38.8%, and 43.6% of surface ozone in the Baltimore, MD, region during July 2002, 2011, and 2018 means, respectively. This unintended consequence of air quality regulation impacts attainment of the National Ambient Air Quality Standard for surface ozone because the spatial and temporal scales of photochemical smog increase; the influence of pollutants transported between states and into the eastern U.S. will likely play a greater role in the future.

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Comparison of background ozone estimates over the western United States based on two separate model methodologies

Cited by 55 publications

References 30 publications

CAMx ozone source attribution in the eastern United States using guidance from observations during DISCOVER‐AQ Maryland

CAMx ozone source attribution in the eastern United States using guidance from observations during DISCOVER‐AQ Maryland

Contributions of foreign, domestic and natural emissions to US ozone estimated using the path-integral method in CAMx nested within GEOS-Chem

Evidence for an increase in the ozone photochemical lifetime in the eastern United States using a regional air quality model

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