Responses in Ozone and Its Production Efficiency Attributable to

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Responses in Ozone and its Production Efficiency Attributable to Recent and Future Emissions Changes in the Eastern United States Lucas Henneman, Cong Liu, Huizhong Shen, Yongtao Hu, James A Mulholland, and Armistead G Russell Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.7b04109 • Publication Date (Web): 07 Nov 2017 Downloaded from http://pubs.acs.org on November 7, 2017

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RESPONSES IN OZONE AND ITS PRODUCTION EFFICIENCY

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ATTRIBUTABLE TO RECENT AND FUTURE EMISSIONS CHANGES IN

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THE EASTERN UNITED STATES

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Lucas RF Henneman* Harvard University T.H. Chan School of Public Health Boston, MA, 02445, USA

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Huizhong Shen School of Civil and Environmental Engineering, Georgia Institute of Technology Atlanta, GA, 30332, USA

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Cong Liu School of Energy and Environment, Southeast University, Nanjing, China

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Yongtao Hu, James A Mulholland, and Armistead G Russell School of Civil and Environmental Engineering, Georgia Institute of Technology Atlanta, GA, 30332, USA

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Abstract Ozone production efficiency (OPE), a measure of the number of ozone (O3) molecules

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produced per emitted NOX (NO + NO2) molecule, helps establish the relationship between NOX

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emissions and O3 formation. We estimate long-term OPE variability across the eastern United

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States using two novel approaches: an observation-based empirical method and a chemical

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transport model (CTM) method. The CTM approach explicitly controls for differing O3 and

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NOX reaction products (NOZ) deposition rates, and separately estimates OPEs from on-road

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mobile and electricity generating unit sources across a broad spatial scale. We find lower OPEs

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in urban areas and that average July OPE increased over the eastern United States domain

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between 2001 and 2011 from 11 to 14. CTM and empirical approaches agree at low

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NOZ concentrations, but CTM OPEs are greater than empirical OPEs at high NOZ. Our results

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support that NOX emissions reductions become more effective at reducing O3 at lower

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NOZ concentrations. Electricity generating unit OPEs are higher than mobile OPEs except near

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emissions locations, meaning further utility NOX emissions reductions will have greater per-unit

35

impacts on O3 regionally.

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Introduction

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Atmospheric scientists have understood the underlying mechanisms of ozone (O3)

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production for decades; however, air quality managers continue to have trouble meeting ozone

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standards, particularly as they have been tightened in response to health studies identifying

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impacts at lower concentrations1,2. The availability of detailed ambient monitoring data and

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much expanded modeling capabilities presents an opportunity for a detailed assessment of the

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effectiveness of past and future air quality improvement efforts. An assessment of this type—

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investigating changing factors attributable to air quality controls—serves to link accountability

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assessments (i.e., efforts to quantify the impacts of existing regulatory programs) to proposed air

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quality programs.

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Largely as a result of air quality management policies, anthropogenic emissions of two

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contributors to ozone concentrations—nitrogen oxides (NOX = NO + NO2) and volatile organic

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compounds (VOCs)—have decreased throughout the eastern United States over the past two

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decades, particularly in urban areas3,4. Emissions reductions, particularly in electrical generating

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unit (EGU) and mobile source sectors, have led to reduced levels of ambient NOX, NOX reaction

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products (NOZ = total atmospheric oxidized reactive nitrogen (NOY) minus NOX), and VOCs5,6.

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These reductions have been linked to changing O3 concentrations—both reductions (primarily in

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the summer) and increases (primarily in the winter and in VOC-limited urban areas)1. Multiple

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studies have found that meteorology has a major impact on daily O3 concentrations5,7,8, and

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climate variability and synoptic patterns impact longer-term trends9,10.

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Ozone production efficiency (OPE) has historically been a popular method for reporting

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the relationship between O3 and NOX. OPE is defined as “the number of molecules of O3 formed

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per each NOX molecule removed from the system”11. NOX generates O3 as it cycles between NO

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and NO2, and since each NOX molecule that enters the system must leave, the emissions rate

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approximates the loss rate across a large scale. The reaction of NO2 with OH—which forms

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nitric acid (HNO3)—is typically the dominant pathway for NOX loss, though it can combine with

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organics or form HNO3 via N2O5 hydrolysis (which occurs primarily at night)12. OPE has

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important policy implications, since it provides an estimate for how much less ozone would be

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formed for a specified reduction in NOX emissions13,14.

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Previous studies using both observation-based and chemical transport model (CTM)-

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based approaches have found increasing OPEs as NOX or NOZ decreases13,15. This increase

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carries the implication that NOX emissions reductions become increasingly effective. There has,

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however, been little investigation of OPE at very low NOZ concentrations. We set out to fulfil

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two objectives: 1) assess multiple observation and CTM-based approaches for estimating total

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and source-specific OPEs in the eastern United States, and 2) project impacts of continuing NOX

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emissions reductions on future OPEs and O3 concentrations.

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Methods

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Observation-based OPE

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We use observations from the SouthEastern Aerosol Characterization (SEARCH)

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network sampling sites16,17, which provide meteorological data, O3, NOX, and NOY species

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observations at eight locations from 1996-2015 (not all sites were online for the entire period).

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The eight locations comprise four pairs: three urban-rural and one urban-suburban. Throughout

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this manuscript, we refer to the sites by their three-letter acronym and a superscript that denotes

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if the site is in an urban (U), rural (R) or suburban (S) location. The southeastern United States is

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characterized by hot, humid summers with periods of stagnation interrupted by large

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thunderstorms. Winters are typically mild and drier, though extended periods of wet weather

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accompany frontal activity. Isolated city centers and power plants, situated throughout mixed

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coniferous and deciduous forests and agricultural land, serve as major air pollution sources16.

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Previous studies have used the relationship between O3 and NOZ as an empirical estimate

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of OPE; in a single plume, the loss of NOX equals the production of NOZ when the deposition

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rate of NOX is negligible18. Kleinman et al. (2002) noted that a major limitation to using a fixed-

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site monitor to estimate OPE is that air masses crossing a monitor have potentially very different

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histories15. Take, for instance, the case of a monitor near a city; at times, it would be downwind

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of the city; O3 (and NOZ) levels would be high as the city contributes fresh emissions that lead to

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high ozone levels, but the NOZ would have had little time to deposit. At other times, the monitor

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would be upwind, the air masses impacting the monitor would have lower ozone, and much of

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the NOZ will have been lost to deposition. Relating measured O3 to NOZ in this case is not

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indicative of the OPE.

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We controlled for air parcel history by stratifying days based on their photochemical

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activity using an ensemble of observations across years. Days with high photochemical activity

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have similar meteorological characteristics, meaning changes in O3 levels should relate to

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changes in NOZ, though other emissions changes can also play a role. We used long-term

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meteorological detrending of ozone observations at SEARCH sites to identify the 20% of days

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with all the relevant data available at each site that were most photochemically active.

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Photochemical state (PS*, described in detail in Henneman et al. (2017)19) was estimated using

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the meteorological detrending method presented in Henneman et al. (2015)7. In brief, observed

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maximum daily average 8-hr (MDA8h) O3 was split into five trends with different periods: long-

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term (period > 365 days), seasonal (365 days), week-holiday (7-365 days), short-term

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meteorological (< 365 days), and white-noise (1 day). The long-term and seasonal components

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were separated using the low-pass moving average KZ filter20. We regressed the remaining

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fluctuations against similarly filtered daily fluctuations in various metrics: weekday and holiday

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factors along with 0, 1, and 2-day lags each of solar radiation (total and daily max), temperature

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(mean and daily maximum), wind speed (morning and daily means), relative humidity, rainfall.

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We calculate the short-term meteorological trend as the fit of meteorological covariates in this

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regression. PS* is the sum of the seasonal and short-term meteorological trends (Figure S1), and

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represents an emissions-independent estimate of daily atmospheric photochemical activity. This

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metric is positively correlated with temperature and wind speed and negatively correlated with

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relative humidity.

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For comparison, we calculated an alternative to the PS* metric (HNO3/NOY), which was

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found to be likely impacted by long-term emissions changes (SI text and Figures S2 and S3);

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therefore, PS* was used as the final metric for photochemical plume age. Since PS* was

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calculated using MDA8h O3, it is indicative of 8-hr averaged photochemical states. We restrict

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the observation-based OPE analysis to days in the upper 20th percentile for PS*, and use 2-3 pm

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averages (local time) for daily metrics of all observed species. We investigated the distribution of

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PS* days and found little evidence that decreasing emissions across the study period impacted

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PS* levels or their likelihood of meeting the top 20% criteria (Figures S4 and S5). We used only

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data from the middle afternoon in the primary analysis to approximate the time of maximum

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photochemical activity21,22. NOZ was calculated as the difference between NOY and NOX.

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We tested multiple models for the O3-NOZ relationship, including linear and log models.

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Models of these types, however, are limited in their application to very low NOZ values, where

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the slope ( ⁄ —estimated with a spline-based regression model) increases and data is

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sparse. Spline models separate the domain of the independent variable into sections, and fit

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curves to each section23. The sections are divided at ‘knots’, and fitted curves in each of the

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sections are required to be continuous at the knots. We applied cubic splines, which extend the

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constraints to include equal first and second derivatives at each knot (Figure 1). We take the

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marginal OPE—i.e., the additional amount of ozone formed due to an additional amount of

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NOZ—as the derivative of fitted O3 vs. NOZ models at each site. The spline model captures slope

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changes at multiple points and does not result in nonphysically high slopes at low NOZ (which

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were found using a log-based regression model). Slopes (i.e., OPEs) are constant outside the

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outermost knots. We employed the the rcs and ols commands from the rms package in R version

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3.3.3 for the spline modeling24.

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Figure 1. Empirical OPE fits using natural spline with 3 knots. Urban sites are on the left, and rural/suburban sites are on the right; each urban/rural (or suburban) pair is matched right to left. Points are colored by the slope of the calculated curve at that NOZ level (i.e., their OPE). Models are fit for days with highest 20% PS*. Chemical Transport Model-based OPE

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We used the Community Multiscale Air Quality (CMAQ) model with the direct

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decoupled method (DDM)25 to simulate air quality and first-order sensitivities to emissions over

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a 12 km domain covering the eastern United States (Figure 2). The model setup and application

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was first described in Henneman et al. (2017) and evaluated in detail in Henneman et al.

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(2017b)19,26. We used results from the 2001 and 2011 simulations described in our previous work

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along with two additional month-long cases with July 2011 meteorology and domain-wide NOX

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emissions reduced by 50% and 90%. The latter simulations serve to control for meteorology and

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all other emissions sources completely while reducing NOX emissions. We preserve consistency

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with observations by using 2-3 p.m. averages, and focus on July because it is a summer month

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with typical conditions suitable for O3 formation.

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Figure 2. CMAQ modeling domain and SEARCH sites colored by spline fit intercept (i.e., regional background O3). Acronyms: Jefferson Street in Atlanta, Georgia (JST); Yorkville, Georgia (YRK); Birmingham, Alabama (BHM); Centerville, Alabama (CTR), Gulfport, Mississippi (GFP); Oak Grove, Mississippi (OAK); Pensacola, Florida (PNS); and Pensacola outlying aircraft landing field, Florida (OLF). Superscripts denote urban (U), rural (R), and suburban (S) sites.

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For a first estimate of CTM-simulated OPE, we used a brute force (BF) method that

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assumes linear OPEs across changing NOZ concentrations similar to previous approaches21,27: ∆

 ∆



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Equation 1

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Where NOZ is the sum of NOX reaction products—peroxyacetyl nitrate (denoted PAN and

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PANX in the default CMAQ CB05 chemical mechanism setup28), peroxynitric acid (PNA),

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organic nitrate (NTR), nitric acid (HNO3), nitrous acid (HONO), nitrate radical (NO3), aerosol

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nitrate in three modes (ANO3I, ANO3J, ANO3K), and dinitrogen pentoxide (N2O5)—as N (e.g.,

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N2O5 contains two N molecules). The changes in O3 and NOZ (∆) are matched by site and date

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across CMAQ simulations. We produced two OPEBF estimates per day at each grid cell in the

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domain: one each for the differences (2011  − 2011%  ) and (2011%  −

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2011%  ).

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We compare  with a second estimate using the ratio of O3 and NOZ sensitivities to on-road mobile (MOB) and electricity generating unit (EGU) source emissions: 

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!"#$



% ,'%( )% ,*+,

Equation 2

-% ,'%( )-% ,*+,

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Where each sensitivity term (S) is in ppm. This equation estimates the ratio of the number of O3

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molecules produced per NOZ molecule attributable to emissions from two sources. We focus on

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EGU and mobile sources because they make up the majority of anthropogenic NOX emissions.

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Equation 2, however, has one important omission; it does not account for O3 and NOZ produced

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that deposits out (indeed, none of the empirical or chemical transport model methods described

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to this point explicitly account for deposition). We rectify this by adding source-specific

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deposition terms: 

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/*0 /*0 .% ,'%( )% 1).% ,*+, )% 1  ,'%(  ,*+,

Equation 3

/*0 /*0 .-% ,'%( )-% ,'%( 1).-% ,*+, )-% ,*+, 1 



"#$

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Where 23,4 is the sensitivity of species i deposition to emissions from source j (again, all

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sensitivity terms are in ppm). CMAQ-DDM calculates the total (wet and dry) O3 deposition (e.g.,

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5 ∗ for O3) and the sensitivities to total deposition from mobile (289 ) and EGU (289 )  ,7  ,:;

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sources. Deposition sensitivities, however, are in units of mass deposited per area; therefore, they

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require a conversion to concentration units. We achieve this for each source and species by

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) by the total deposition (e.g., dividing the source-specific deposition sensitivity (e.g., 289  ,7

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5 ∗ ) and multiplying by the concentration. For example:

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289  ,7



/*0 %  ,'%(

89%∗ 

∗ 

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The CMAQ deposition-corrected sensitivities method presents a further opportunity—

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estimating source-specific OPEs—achieved by calculating ratios of mobile and EGU

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sensitivities, respectively:  7

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 :;

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/*0 .% ,'%( )% 1  ,'%(

/*0 .-% ,'%( )-% ,'%( 1

Equation 5



/*0 .% ,*+, )% 1  ,*+,

/*0 .-% ,*+, )-% ,*+, 1

Equation 6



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Where Equations 2-3 and 5-6 produce daily OPE estimates at each grid cell for each CMAQ

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simulation.

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Approach limitations

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Various assumptions are inherent in the methods discussed in this study. The empirical

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method effectively uses an Eulerian framework, i.e., stationary measurements of plumes with

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different histories and degrees of photochemical activity. The approach is subject to varying

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background pollutant levels, nighttime nitrate chemistry, air parcel transport, varying NOZ and

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O3 deposition rates, and clouds14. A Lagrangian approach, i.e., when observations are made

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while traveling with a specific air mass, does not suffer from effects from air parcels with

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varying histories, and has been undertaken using aircraft14,15,29–31. The Lagrangian approach

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typically has led to lower OPEs than those found using Eulerian approaches, although this may

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be due to higher NOZ concentrations in plumes, a more limited time to react, or because the

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typical approach is to correlate O3 with NOZ instead of taking the nonlinearity of the system into

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account18,30,32,33.

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The empirical approach is further limited because it does not control for changing

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emissions of other species involved in ozone formation (e.g., VOCs and CO) or long term

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changes in the meteorology leading to high ozone days (e.g., due to climate change). Sensitivity

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studies find that O3 is primarily sensitive to NOX in the Southeast on high ozone days34,35, though

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results in downtown areas suggest negative sensitivity to local NOX emissions and VOC

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sensitivity even on high ozone days1,35,36. Urban NO emissions inhibit O3 production, albeit

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primarily on days with low photochemical activity11.

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Recent work has shown that a portion of NOX in the Southeast may react with organics and hydrolyze to form organic nitrogen aerosol. Fisher et al. (2016) found that 21% of NOX was

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lost to RONO2, and that this fraction increases with decreasing NOX emissions37. 59% of RONO2

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was hydrolyzed to aerosol in the southeastern US in August-September 2013. This portion of

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NOZ is not present in NOY measurements, and would lead to increasing bias in the empirical

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OPE model at low NOZ concentrations.

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A limitation of the CMAQ approach is the use of concentrations, deposition, and

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sensitivities to anthropogenic emissions that are found at the same location/cell (an Eulerian

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frame), whereas each of these would be impacted by transport and deposition between the

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NOX source location and the location of interest. Total O3 and NOZ deposition fields are spatially

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similar to, but smoother than, their respective concentration fields—with the exception of

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localized NOZ deposition hot-spots associated with wet deposition of aerosol NO3—suggesting

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this issue is of limited importance when using sensitivity-based approaches, particularly if

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deposition is accounted for in the OPE calculations (Figures S6 and S7 show modeled O3

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concentrations and deposition).

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Results and Discussion

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Observation-based OPE

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We compared mean 2-3 p.m. O3 against mean 2-3 p.m. NOZ for days with PS* greater

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than the 80th percentile. The resulting relationships have positive slopes and negative second

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derivatives, corroborating results from previous studies38,39 (Figure 1, Figure S8 for results

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obtained selecting a wider PS* range). NOX and NOZ levels decreased across the study period,

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driven by reductions in NOX emissions5,19 (Figure S9). OPE results from linear and log models

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are presented in the supporting information (Figure S10; further, Blanchard et al. (2017)

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investigate OPE at SEARCH sites using linear models and other methods22); here we focus on

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the cubic spline model results.

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Empirical model intercepts at each of the sites represent an estimate of local background

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concentrations, i.e., expected concentrations when NOZ levels are quite small due to low local

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NOX emissions and/or nearly complete NOZ deposition. Local background ozone results from

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global and regional emission sources—not local sources—although it can be generated nearby.

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For example, urban sources may affect surrounding background ozone, resulting in elevated rural

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background ozone nearby and increased titration in cities further downwind. These intercepts

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likely represent an upper bound of the background for two reasons. First, the spline model’s

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restriction to linearity outside the outermost knots is rather stringent, and may miss small slope

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increases at the lowest NOZ concentrations. Second, differences between O3 and NOZ deposition

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are magnified at low concentrations; accounting for this would shift the points plotted in Figure

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S1 rightward, thus creating a lower intercept.

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The estimated level that O3 levels would reach in the absence of local NOX emissions

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varies for each site with meteorological conditions and long-term climate and emissions changes.

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Using a Monte Carlo sampling technique, we fit multiple spline models at each site using

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intercepts selected from the normal distribution around the mean estimated in the original model

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fit, and calculated an uncertainty distribution of the slopes at each NOZ concentration (Figure 3

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and S11). This method was chosen because of the high uncertainty in background O3 and OPE at

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low NOZ concentrations and the difficulty in assessing individual model parameter error in spline

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models. Uncertainty in OPE is larger at sites closer to the Gulf of Mexico.

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Figure 3. OPE at each SEARCH site estimated by the empirical spline and CMAQ depositioncorrected sensitivities approaches. Empirical values represent all days with PS* > 80th percentile, and CMAQ values are days in July. Grey shading is the 95% confidence interval around the spline model. All O3-NOZ spline model intercepts fall between 30.2 (GFPU) and 47.2 ppb (OAKR)

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(Figure 2). Urban sites tend to have lower intercepts than rural sites, and the two sites closest to

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the Gulf of Mexico (GFPU & PNSU) have the lowest intercepts (i.e., local background O3) with

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the greatest uncertainty. CTRR, in Alabama, stands out as the rural site with the lowest intercept.

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Background estimates align with the upper end of previous estimates of the levels that ozone

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would reach in the absence of NOx emissions in the Southeast. Emery et al. (2012) found very

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few days on which policy-relevant background—i.e., absent North American emissions—

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MDA8h O3 exceeded 40 ppb in 200640, and Lefohn et al. (2014) found estimates of background

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levels to be less than about 30 ppb in Atlanta41. These previous estimates used air quality models

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to estimate background O3; the present approach uses observed data to estimate regional

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background levels, which are expected to be somewhat more variable31. Elevated intercepts at

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rural sites (e.g., YRKR and OAKR) suggest impacts from nearby urban centers, which

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corroborates previous findings by Blanchard et al. (2013, 2014)34,42, though the lack of urban-

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rural difference between BHMU and CTRR again suggests some level of uncertainty.

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Empirical OPEs at JSTU and YRKR, two sites in Georgia, have similar shapes (Figure 3),

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and OPEs at the rural site are slightly higher than OPEs at the urban site. This trend repeats at

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BHMU and CTRR, with the rural site (CTRR) exhibiting greater OPEs across all NOZ levels. The

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remaining sites (GFPU, OAKR, PNSU, OLFS) have OPEs that differ from the urban-rural

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relationships at the four inland sites. Limited data availability for these sites contributes to larger

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uncertainty bounds. GFPU, OLFS and PNSU are near the Gulf of Mexico; the air from the Gulf

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causes OPEs at these sites to increase dramatically at low NOZ because ozone is efficiently

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transported over the water with relatively little deposition, while nitric acid is very soluble and

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reacts with sea salt, leading to larger particles that readily deposit. Neuman et al. (2009) suggest

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high variability in regional background depending on wind direction in Houston; these results

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reflect large uncertainties in coastal sites compared to inland found in the present study31. OLFS,

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a suburban site somewhat inland from PNSU, has a higher OPE at elevated NOZ levels and lower

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OPE at low NOZ levels than PNSU. The two sites nearest the Gulf of Mexico (GFPU & PNSU)

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have the lowest intercepts and the highest OPEs at low NOZ.

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Chemical Transport Model-based OPE

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For two July 2011 comparisons—i.e., between the change from base emissions to 50%

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NOX emissions and from 50% NOX emissions to 10% NOX emissions—correlation analysis of

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OPEnodep versus OPEBF finds a slope near one and R2 of 0.39 and 0.27, respectively (Figure S12).

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The comparison controls for meteorology, but neither controls for deposition. A potential source

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of bias in OPEnodep is the exclusion of higher order sensitivities; Cohan et al. (2005) showed that

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second order effects could contribute ~30% bias. Still, the slopes near one support the

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sensitivity-based approach.

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Without correcting for deposition, over-land average July 2001 and 2011 OPEs were 10.9

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and 13.8, similar to the with-deposition values (Table 1). Remaining discussions focus on

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deposition-corrected OPEs (Equations 3, 5, and 6); all summary statistics are reported for the

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portion of the domain encompassed by the superimposed polygon in Figure 4 to exclude

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boundary effects and impacts of high OPEs over water.

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Table 1. Information on the modeling runs. Emissions are summed over the entire domain, while concentrations and OPEs are averaged over the polygon superimposed on the inland region of each of the plots in Figure 4 to exclude boundary effects and high OPEs over water. July NOX Emissions, 103 tons ALL

MOB

EGU

Mean July Concentrations, ppb (std. dev.) O3

NOZ

Mean July OPE (std. dev.)

NOX

OPE

OPEnodep

OPEMOB

OPEEGU

2001

1,364

773

432

54.7 (4.7)

2.7 (0.9)

1.6 (2.7)

11.2 (2.9)

10.9 (3.1)

10.7 (3.0)

12.7 (3.1)

2011

679

364

169

48.1 (5.5)

1.9 (0.6)

1.1 (1.3)

14.0 (3.3)

13.8 (3.3)

13.8 (3.5)

15.3 (3.6)

2011 50% NOx

339

182

85

39.5 (4.7)

1.1 (0.4)

0.5 (0.6)

15.7 (2.3)

15.7 (2.4)

2011 10% NOx

68

36

17

28.6 (3.6)

0.3 (0.1)

0.1 (0.1)

16.4 (1.7)

16.4 (1.7)

309

310 311 312

A) 2001 OPE

B) 2001 OPEMOB

C) 2001 OPEEGU

D) 2011 OPE

E) 2011 OPEMOB

F) 2011 OPEEGU

Figure 4. CMAQ-modeled mean daily July deposition-corrected OPE (2-3 p.m.) values for 2001 (A-C) and 2011 (D-F), including total OPE (A&D), mobile OPE (B&E), and EGU OPE (C&F).

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CMAQ simulations yielded consistently higher OPEs in rural areas than urban areas, and

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generally decreasing OPE with increasing latitude (Figure 4). Between 2001 and 2011, average

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over-land OPEs increased from 11.2 to 14.0 (Table 1), while OPEs at SEARCH locations

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increased from 9.6 to 12.7 (Figure S13, Table S1). Between 2001 and 2011, estimated domain-

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wide NOX emissions decreased 50% and mean overland OPE increased 25%. A hypothetical

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50% decrease in NOX emissions would lead to only an additional 15% increase in OPE, and a

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further 80% emissions cut (90% total reduction from base 2011) would increase OPE just 4%

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more (Figure S14). Average July overland OPEs do not exceed 25, even at the most extreme

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(90%) NOX emission reduction (Figure S15). Even on July days with the 2nd-highest simulated

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O3 concentrations in each location, OPE does not exceed 27. These results show that OPE is

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expected to increase at a decreasing rate with further emission reductions, and corroborate the

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empirical results that show that OPE increases to location-specific upper limits at lower NOX.

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Mobile and EGU OPEs differ spatially. Mobile OPEs are slightly lower than the total

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OPEs in 2001 and 2011 (Table 1). Their spatial pattern generally mirrors that of total OPE, with

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lower values near concentrated mobile emissions in urban centers and along interstates (Figure

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4). EGU OPEs are generally greater than the total OPEs, with low spots centered near individual

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plants.

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CMAQ OPE results agree with empirical OPEs at low NOZ concentrations for most sites,

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although CMAQ OPE estimates at rural sites are slightly higher (Figure 3). Note that in this

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comparison, empirical results use days across the entire period with PS* > 80th percentile, and

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CMAQ values use only July days in 2001 and 2011. Further, the three coastal sites (GFPU, OLFS

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and PNSU) are not located within the overland domain used for analysis (Table 1), and direct

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comparisons between the methods are inherently problematic because both are models with

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uncertainties and bias.

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CMAQ-modeled domain-wide average O3 concentration in the 10% NOX scenario, at

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28.6 ppb, is lower than the background O3 concentrations estimated by the empirical approach.

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While the comparison here is not direct, it does again suggest that the empirical methods

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estimate a regional (instead of a global) background that is still influenced by non-local

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emissions.

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Implications The empirical and CMAQ-based methods applied here were designed to reduce biases

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associated with the limitations discussed above. In the empirical method, the use of

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meteorologically similar days with high photochemical activity (PS*) across multi-year datasets

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limits (but likely does not remove) effects of plumes with markedly different origins. We

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assessed OPE uncertainty based on a distribution of statistically modeled background O3 levels,

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and found the largest impacts at coastal sites. Using CMAQ-DDM, we controlled for air parcel

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transport and differing O3 and NOZ deposition rates by applying source-specific concentration

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and deposition sensitivity ratios instead of just concentrations.

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Rising OPEs with decreasing NOZ concentrations suggests a NOX-limited regime, with

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increasing effectiveness for reducing O3 concentrations for each subsequent avoided NOX

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molecule emitted. We show this for the empirical models by integrating under the OPE curves in

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Figure 3. For a 0.5 ppb NOZ concentration decrease, the expected change in O3 concentrations

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(∆O3) increases with decreasing starting NOZ concentrations for all sites (Figure 5). That ∆O3

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reaches a maximum at the lowest NOZ concentrations is somewhat a function of the choice of

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spline model; however, CMAQ results provide evidence for a maximum OPE (and subsequently

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a maximum ∆O3 in response to emissions decreases). 2011 OPEs across the domain and at all

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SEARCH sites increased at a decreasing rate with NOX emissions reductions (Table S1, Figure

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S13); at CTRR, GFPU, and OAKR, the reduction from 50% to 10% NOX emissions led to slight

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decreases in OPE, suggesting a plateauing OPE.

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Figure 5. Change in O3 expected from a 0.5 ppb reduction in NOZ from observed NOZ at each site for days in July.

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We tested the agreement between the empirical spline model and CMAQ-modeled

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changes in O3 for a 50% NOX emissions reduction at the SEARCH sites (Figure S16). For

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relative reductions (as opposed to absolute reductions addressed above), both models find

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decreasing effectiveness at lower starting NOZ concentrations (since relative reductions at lower

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total emissions refer to increasingly small mass-based emissions). The models generally agree

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for all sites. The comparison assumes that NOX emissions reductions directly correlate with NOZ

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concentration reductions, which is a close (though not perfect) approximation; NOZ

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concentrations are linear with reductions using consistent meteorology, but are nonlinear

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between 2001 and 2011 due to changing meteorological conditions (Table 1, Figure S14).

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CMAQ results yield strong OPE differences between rural and urban areas, and a

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lesser—though still significant—trend with decreasing latitude (Figures S17 and S18). We found

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negative relationships between OPE and both NOZ and formaldehyde (HCHO—a reaction

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product of volatile organics). We found a positive relationship between OPE and the organic

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fraction of NOZ (fN,org), which explains variability consistent with change across latitude (see

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discussion in the supporting information).

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Empirical and CMAQ results provide evidence that maximum OPEs are site-specific,

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reflecting site-specific meteorologies and emissions of species other than NOX. At the lowest

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NOZ concentrations, the potential for reducing O3 concentrations through NOX emissions

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reductions levels off; however, O3 concentrations have a positive relationship with NOZ at all

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NOZ concentrations. This suggests that continuing emissions controls will reduce O3

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concentrations at increasing rates, but the effect will level off at location-specific NOZ

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concentrations.

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EGU OPEs are on average greater than mobile OPEs across the domain. This suggests

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greater expected O3 reductions per absolute NOX emissions reduction from EGUs than mobile.

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However, this comparison does not consider the amount of emissions left to reduce—mobile

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sources emitted more than 2X NOX than EGU sources in 2011. Since modelled 2011 EGU OPE

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is 10% larger than mobile OPE (15.3 vs. 13.8), more O3 reduction is available through

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proportional mobile emission reductions than EGU.

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This study shows that future NOX emissions reductions remain a viable option for

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reducing the highest O3 concentrations. OPE increases with decreasing NOZ concentrations, but

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appears to level out or reach a maximum, which implies that incremental NOX emissions

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reductions will become more effective at reducing O3 up to a point, after which the incremental

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benefit will level off. Empirical results based on ambient observations suggest that maximum

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OPE has already been attained in rural areas, and results obtained using the chemical transport

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modeling approach corroborate this finding.

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Acknowledgements

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This material is based upon work supported by Health Effects Institute and the National

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Science Foundation Graduate Research Fellowship under Grant No. DGE-1148903. This

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publication was developed under Assistance Agreement No. 83588001 awarded by the U.S.

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Environmental Protection Agency to Georgia Institute of Technology. It has not been formally

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reviewed by EPA. The views expressed in this document are solely those of the authors and do

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not necessarily reflect those of the Agency. EPA does not endorse any products or commercial

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services mentioned in this publication. We are grateful for discussions with Charlie Blanchard

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regarding measurements made at SEARCH network sites.

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Supporting Information Available

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Supplemental analyses, 18 figures and two tables.

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References

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