modeled aerosol nitrate formation pathways during wintertime in … · 2016-07-05 · modeled...

Modeled aerosol nitrate formation pathwaysduring wintertime in the Great Lakesregion of North AmericaYoo Jung Kim1, Scott N. Spak1,2, Gregory R. Carmichael1,3, Nicole Riemer4, and Charles O. Stanier1,3,5

1Center for Global and Regional Environmental Research, University of Iowa, Iowa City, Iowa, USA, 2Public Policy Center,University of Iowa, Iowa City, Iowa, USA, 3Department of Chemical and Biochemical Engineering, University of Iowa, IowaCity, Iowa, USA, 4Department of Atmospheric Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA,5IIHR Hydroscience and Engineering, University of Iowa, Iowa City, Iowa, USA

Abstract Episodic wintertime particle pollution by ammonium nitrate is an important air quality concernacross the Midwest U.S. Understanding and accurately forecasting PM2.5 episodes are complicated bymultiple pathways for aerosol nitrate formation, each with uncertain rate parameters. Here, the CommunityMultiscale Air Quality model (CMAQ) simulated regional atmospheric nitrate budgets during the 2009 LADCOWinter Nitrate Study, using integrated process rate (IPR) and integrated reaction rate (IRR) tools to quantifyrelevant processes. Total nitrate production contributing to PM2.5 episodes is a regional phenomenon, withpeak production over the Ohio River Valley and southern Great Lakes. Total nitrate production in the lowertroposphere is attributed to three pathways, with 57% from heterogeneous conversion of N2O5, 28% fromthe reaction of OH and NO2, and 15% from homogeneous conversion of N2O5. TNO3 formation rates variedday-to-day and on synoptic timescales. Rate-limited production does not follow urban-rural gradients and NOx

emissions due, to counterbalancing of urban enhancement in daytime HNO3 production with nocturnalreductions. Concentrations of HNO3 and N2O5 and nighttime TNO3 formation rates have maxima aloft(100–500m), leading to net total nitrate vertical flux during episodes, with substantial vertical gradients innitrate partitioning. Uncertainties in all three pathways are relevant to wintertime aerosol modeling andhighlight the importance of interacting transport and chemistry processes during ammonium nitrate episodes,as well as the need for additional constraint on the system through field and laboratory experiments.

1. Introduction

Particulate matter smaller than 2.5μm, also known as fine particulate matter or PM2.5, is an importantenvironmental health risk factor [Schwartz and Dockery, 1992; Dockery and Pope, 1994; Laden et al., 2000; Popeet al., 2009; Smith et al., 2009]. To protect public health as directed under the Clean Air Act, the UnitedStates Environmental Protection Agency (EPA) has established ambient concentration standards. Attainmentunder the Clean Air Act is determined using an annual standard (currently set at 12μg/m3) as well as ashort-term (24 h) standard, which is currently 35μg/m3.

The Upper Midwest region of the United States, defined for this work as the states of Minnesota, Iowa,Wisconsin, Illinois, Indiana, and Michigan, frequently experiences persistent multiday episodes of wintertimePM2.5 pollution that approach or exceed the short-term standard. These widespread regional wintertimeepisodes are characterized by a large contribution to PM2.5 from ammonium nitrate [McMurry et al., 2004;Blanchard and Tanenbaum, 2008; Pitchford et al., 2009]. The ammonium nitrate fraction in PM2.5 reaches50–60% during February episodes, relative to an annual average of 30–40% [Katzman et al., 2010]. Urbanenhancement of some pollutants (e.g., elemental and organic carbon) has been shown; however, the mainsecondary species during these episodes, ammonium nitrate and ammonium sulfate, have been shown tohave minimal urban enhancement [Stanier et al., 2012]. Using back trajectory analysis, Zhao et al. [2007]hypothesized that nitric acid from a mixture of urban sources and from the Ohio River valley, mixed with awider regional ammonia source to create the wintertime ammonium nitrate pollution in the Midwest.

Recurrent cold weather PM2.5 episodes are not confined to the Upper Midwest of the U.S.; they have alsobeen documented in Northwestern and Central Europe [Schaap et al., 2002], Hong Kong [Louie et al., 2005],Tokyo, Japan [Shimadera et al., 2014], and in mountain basins in the Po Valley of Italy [Schaap et al., 2004;

KIM ET AL. ©2014. American Geophysical Union. All Rights Reserved. 1

PUBLICATIONSJournal of Geophysical Research: Atmospheres

RESEARCH ARTICLE10.1002/2014JD022320

Key Points:• Regional HNO3 production is dominatedby nocturnal conversion of N2O5

• Heterogeneous and homogeneousHNO3 production maxima at boundarylayer top

• Total nitrate production duringepisodes highest over Great Lakes andOhio Valley

Supporting Information:• Readme• Text S1, Tables S1–S4, and Figures S1–S8

Correspondence to:C. O. Stanier,[email protected]

Citation:Kim, Y. J., S. N. Spak, G. R. Carmichael,N. Riemer, and C. O. Stanier (2014),Modeled aerosol nitrate formationpathways during wintertime in theGreat Lakes region of North America,J. Geophys. Res. Atmos., 119, doi:10.1002/2014JD022320.

Received 16 JUL 2014Accepted 30 SEP 2014Accepted article online 3 OCT 2014

http://publications.agu.org/journals/

http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)2169-8996

http://dx.doi.org/10.1002/2014JD022320

http://dx.doi.org/10.1002/2014JD022320

Carbone et al., 2010; Putaud et al., 2010], Santiago, Chile [Gallardo et al., 2002; Saide et al., 2011], California’sCentral Valley [Pun and Seigneur, 1999; McMurry et al., 2004; Kelly et al., 2014], Utah’s Salt Lake Valley [Gillieset al., 2010; Lareau et al., 2013], and other mountain basins in the American West [Baker et al., 2011]. Inaddition to the contribution of ammonium nitrate, many studies identify important meteorological driversthat influence episode occurrence and/or episode intensity: low wind speeds, near-freezing temperatures,snow cover, and large-scale inversion conditions [Chu, 2004; Bender et al., 2009; LADCO, 2009; Baek et al., 2010;Spak et al., 2012; Stanier et al., 2012].

Modeling and conceptual understanding of wintertime episodes are complicated by the fact that multiplepathways compete for NOx oxidation. Several pathways may be relevant during episode formation periods, butwith different spatial, vertical, and diurnal patterns. The atmospherically relevant pathways of nitric acidformation in the troposphere [Pandis and Seinfeld, 1989;Wu et al., 2008; Zhang et al., 2008] are (1) the reaction ofOH and NO2 (main gas phase pathway), (2) NO3 radical reaction with hydrocarbons, (3) NO3 conversion to N2O5

with subsequent conversion to HNO3, and (4) aqueous processing of the NO3 radical to form HNO3. The firstpathway (OH+NO2) is usually referred to as the daytime pathway because the required OH is severely limitedat night due to the absence of O3 and peroxide photolysis. The remaining pathways are commonly referred toas the nighttime pathway, since both NO3 and N2O5 are photolabile. This work adopts this nomenclature ofdaytime and nighttime pathways. For compact notation, we refer to nitric acid as HNO3, aerosol nitrate as NO3

!,and their sum as total nitrate (TNO3).

The third listed pathway includes both a heterogeneous pathway of N2O5 reacting on and in hydratedaerosol particles, as well as a less favorable homogenous pathway involving the reaction with gas phase H2O[Mozurkewich and Calvert, 1988; Li et al., 1993; Patris et al., 2007; Chang et al., 2011]. Formation of ClNO2 viaheterogeneous reactions of N2O5 is also an area of active research [Brown and Stutz, 2012; Young et al., 2012].We further discuss the potential impact of the Cl reactions on our results, although it was not a focus of thisstudy since heterogeneous Cl reactions were not implemented in the model we employed.

In order to better understand wintertime particle matter episodes in the Upper Midwest, the Lake Michigan AirDirectors Consortium Winter Nitrate Study (LADCO WNS) was conducted in 2009 [Baek et al., 2010]. Thestudy included hourly observations (total NOy, NOx, inorganic aerosol ions, PM2.5, HNO3, ammonia, andmeteorology) and daily measurements of aerosol speciation at an urban-rural pairing of monitoring sites atMilwaukee and Mayville, Wisconsin [Stanier et al., 2012]. Observations reinforced findings previously publishedregarding the meteorological drivers for episodes [LADCO, 2009], including persistent high-pressure systems,stagnant conditions, and strong inversions. Preliminary calculations based on the observed time series ofaerosol nitrate and its precursors, together with mean OH concentrations from a wintertime chemical transportmodel [Grabow et al., 2012], suggested that nighttime chemistry could contribute up to 50% of local nitrateproduction during events [Grabow et al., 2012; Stanier et al., 2012]. One goal of this work is to refine thepreliminary estimate of the contribution of the daytime and nighttime formation pathways at the twoobservation sites of the LADCO WNS, as well as throughout the surrounding region.

In addition to investigating the fraction of nitrate attributable to each pathway using the chemical transportmodel, we also inspect in detail the model output for (1) the relative abundance of the various NOz species(NOz =NOy – NOx, with NOy being the sum of all reactive nitrogen species) other than HNO3 and aerosolNO3

!, (2) the vertical profiles of NOy species [de Brugh et al., 2012], (3) the relative importance and absoluterates of competing NOx oxidation pathways [Dennis et al., 2008;MacIntyre and Evans, 2010; Stanier et al., 2012;Walker et al., 2012; Zhang et al., 2012], and (4) the spatial and vertical extent of HNO3 formation. Thesecontributions are consistent with the research needs formulated by Yu et al. [2005] and Chang et al. [2011].Heald et al. [2012] acknowledged several of these research needs from their finding that GEOS-Chemoverpredicts total nitrate in the Midwest in most seasons due to an overprediction of HNO3 in the EasternU.S., and they hypothesized insufficient alternate pathways for NOy and possibly insufficient HNO3

deposition as possible resolutions.

This work addresses these issues with a focus on wintertime episodes using the Community Multiscale AirQuality (CMAQ) model [Binkowski and Roselle, 2003] and the CMAQ Process Analysis (CMAQ-PA) tool [Jeffriesand Tonnesen, 1994; Tonnesen, 1995; Byun and Ching, 1999; Y. Zhang et al., 2005] applied to simulations of theLADCO WNS. CMAQ-PA is used as an advanced probing tool to elucidate questions of NOx to NOz

Journal of Geophysical Research: Atmospheres 10.1002/2014JD022320


transformation by tracking the contribution of individual reactions as well as contributions of modelprocesses to changes in species-by-species concentrations for every grid cell across the domain (e.g.,influence of dry deposition on HNO3 at a specific model hour and grid cell). Information from CMAQ-PA isnecessary for understanding the underlying reasons for the model predictions, quantifying the controllingfactors for TNO3 concentrations, and also for quantifying the HNO3 formation pathways.

In recent years, CMAQ-PA has been applied to study the formation and removal of ozone for North America[Kimura et al., 2008; Vizuete et al., 2008; Yu et al., 2009; Henderson et al., 2011], Europe [Gonçalves et al., 2009],and Asia [Xu et al., 2008; Wang et al., 2010]. Khiem et al. [2010] used CMAQ-PA to understand the relativeimpacts of vertical mixing, dry deposition, and chemical reactions on ozone concentrations. Others havequantified the relative importance of processes (e.g., primary emissions, transport, cloud processing, etc.) inthe simulation of North American PM [Yu et al., 2008;Wang et al., 2009; Liu et al., 2010]. Zhang et al. [2009] andShimadera et al. [2014] specifically investigated cold season nitrate in the boundary layer. Zhang et al. [2009]reported increases in simulated aerosol nitrate concentrations resulting from calls to the aerosol module,while aerosol nitrate decreases were from cloud and transport processes. Since the CMAQ aerosol moduleincludes both thermodynamic partitioning (e.g., HNO3(g) to NO3

!(p)) as well as heterogeneous conversion ofN2O5 to nitric acid, more sophisticated use of the process analysis tools is required to elucidate pathways foraerosol nitrate formation.

In this work, the CMAQ-PA system is applied to the period January to March 2009, which covers the intensivefield campaign phase of the LADCOWNS. One goal of this work is to quantify the contribution of the daytimepathway (NO2 +OH) versus the nighttime pathway (involving N2O5) to wintertime episode nitrate. Thisquantification is first done integrating over the whole model domain and modeling period. Then the analysisis refined to examine vertical, spatial, and temporal patterns. Section 2 describes the details of the WRFmeteorological simulation, emissions, and chemical transport model used in this work.

Key results include quantification of the daytime vs. nighttime pathway, as well as vertical profiles and modelcross sections for key NOy species and NH3(g). Diurnal patterns in HNO3 formation chemistry are alsoinvestigated. Finally, flux and reservoir diagrams are developed for the two LADCO WNS observation sitesduring daytime, nighttime, episode, and non-episode conditions. These results are used to advance theconceptual model of wintertime nitrate episode evolution.

2. Data and Methods2.1. Modeling System

Our modeling employed recent public release versions for WRF and CMAQ, with configurations as described inthe subsections below, based on guidance from state, regional, and federal agencies. Identical domainswere used for WRF and CMAQ. A 36! 36 km horizontal resolution mother domain covered the continental U.S.,and a 12 km! 12 km horizontal resolution domain was nested over the Upper Midwest. Figure 1 shows theextent of the inner 12 km resolution domain. Thirty-five vertical layers were employed with a model top at50 hPa and increased resolution in the planetary boundary layer (PBL) and near the tropopause. The second

(b)(a)

Figure 1. Modeled concentrations at the surface for (a) PM2.5 over the entire 12 km resolution domain and (b) total nitrate(fine aerosol nitrate + HNO3) over the subdomain (red dotted box) identified as the peak region for total nitrate.



layer face was placed at 20m so that the vertical midpoint of surface grid cells is located 10m above groundlevel. The simulation ran from 1 January to 31March, 2009 with a 10day spin-up period in bothWRFand CMAQ.2.1.1. MeteorologyMeteorological modeling was conducted using version 3.1.1 of WRF-ARW, an update to the system describedby Skamarock et al. [2008]. The WRF configuration used a combination of science modules and parametricsettings recommended by a multi-state and Regional Planning Office collaboration for use in analysessupporting State Implementation Plan development [Baker et al., 2009]. We selected the ACM2 scheme [Pleim,2007a, 2007b] in order to simulate PBL dynamics consistently in both WRF and CMAQ. The combination ofACM2with the Pleim-Xiu land surfacemodel [Xiu and Pleim, 2001] inWRFwas found byGilliam and Pleim [2010]to exceed prior MM5 model performance benchmarks. Additional description of WRF settings can be found inSpak et al. [2012].2.1.2. Chemical Transport ModelChemistry and transport were simulated using CMAQ v.4.7.1, an update to the system described by Byun andSchere [2006]. Science process configuration was identical to contemporary U.S. EPA regulatory analyses,including Carbon Bond Mechanism (CB05) gas phase chemistry [Yarwood et al., 2005], the AERO5 aerosolmodule with ISORROPIA aerosol thermodynamics v.1.7 [Nenes et al., 1998], ACM2 PBL closure [Pleim, 2007a,2007b], and mass-conserving advection and diffusion. Meteorology was processed for use in CMAQ by MCIPv.3.6. Boundary conditions for the 12 km regional domain came from the 36 km continental simulation, forwhich clean hemispheric boundary conditions were used.

The AERO5 module beginning with CMAQ v.4.7 includes updated treatment for heterogeneous N2O5

chemistry that is relevant to this work. Currently, one of themajor uncertainties in air quality modeling of NOy

is the parameterization of the N2O5 heterogeneous reaction probability (γN2O5). CMAQ v.4.6 employedparameters for γN2O5 by Evans and Jacob [2005], categorized by aerosol types: sulfate, organic carbon, seasalt, and dust. The γN2O5 for sulfate, organic carbon, and sea salt included temperature and relative humiditydependence. The γN2O5 for sulfate assumed ammonium sulfate. However, after release CMAQ v.4.6, atypographical error was found in the published equation of Evans and Jacob [2005] by Jerry Davis, whichresulted in γN2O5 increases with increased temperature contrary to laboratory data. Correction of the errorprompted an improved treatment for γN2O5. Accordingly, CMAQ v.4.7 uses a γN2O5 parameterizationdescribed in Davis et al. [2008], which considers a dependence of γN2O5 on inorganic particle composition,temperature, and relative humidity. As a result, the uptake coefficient γN2O5 varies by several orders ofmagnitude depending on aerosol composition [Chang et al., 2011]; therefore, this improvement can besignificant in 3-D modeling of the ambient atmospheric NOy budget. Foley et al. [2010] reported the result ofthe γN2O5 update in tests over the eastern United States. The effect of the correction in the temperaturedependence of the γN2O5 parameterization led to significant increases in January TNO3 in the upper Midwest,while changes to the parameterization (other than the error correction) led to minor decreases.

The thermodynamic partitioning routine in CMAQ, ISORROPIA v.1.7, is critical to ammonium nitrateprediction, because accumulation mode aerosol nitrate can only exist in CMAQ through direct emission(which is minor) or by thermodynamic partitioning of TNO3 in ISORROPIA. Briefly, the thermodynamic modelinputs are relative humidity, temperature, and the sum of gas and aerosol concentrations of sodium, sulfate,ammonium, nitrate, and chloride. The partitioning between the gas and aerosol phases is then calculatedaccording to thermodynamic equilibrium equations with activity coefficient models to account for the highlynon-ideal ionic solutions. The issue of thermodynamic partitioning is critical to the episodic ammoniumnitrate problem due to the rapid deposition of HNO3 compared to NO3

!; accumulation of TNO3 to theconcentrations of concern for air quality is muchmore probable in locations where the partitioning favors theaerosol phase.

Research on heterogeneous N2O5 reactions is ongoing; some important features of the reaction system arenot incorporated in CMAQ 4.7.1, and are therefore not reflected in the model and process analysis results.These features include the inhibition of N2O5 uptake due to organic coatings on particles [Folkers et al., 2003;Thornton and Abbatt, 2005; Anttila et al., 2006; Brown et al., 2006; Riemer et al., 2009], and the heterogeneousreaction of N2O5 and dissolved chloride, which leads to ClNO2 as a product, reducing TNO3 formation[Bertram and Thornton, 2009; Roberts et al., 2009; Sarwar et al., 2012]. Implications for these neglected aspectsof N2O5 chemistry are included in section 3.2.



In order to be comparable to the observed aerosol nitrate (which was limited to PM2.5 nitrate) and theobserved total nitrate (PM2.5 + HNO3(g)), aerosol nitrate concentrations reported herein are limited to fineaerosol nitrate; specifically, PM2.5 nitrate is used for performance statistic calculation, while the sum ofAitken and accumulation mode nitrate is used for all other instances of aerosol nitrate concentrations. Thesum of Aitken and accumulation mode nitrate is used to be consistent with the process analysis tool, whichtracked process-specific contributions to the Aitken and accumulation mode aerosols. In this work, totalnitrate is the fine aerosol nitrate plus the gas phase nitrate.2.1.3. EmissionsEmissions were based on the May 2011 version of the LADCO 2007/2008 Base C anthropogenic inventory[LADCO, 2010], resolving monthly sectoral average emissions and diurnal profiles. For states in the UpperMidwest, which comprise the majority of the regional (12 km resolution) modeling domain, LADCO’s 2007base emissions data were used for agricultural, non-road, and on-road area sources and industrial pointsources. Emissions data for power plants were updated to reported 2008 totals. All sectors include sector-specific hourly weekday and weekend/holiday activity profiles.

The 2007/2008 inventories for on-road, off-road, and agricultural ammonia emissions were estimated using avariety of emission models. EPA’s MOVES2010a model was used with national default inputs to produceon-road emissions for the country. EPA’s NMIM2008 model was used to produce emissions for most off-roadsources. Emissions for three other off-road categories (commercial marine, aircraft, and rail) were developedseparately. Agricultural ammonia emissions were based on Carnegie Mellon University’s 2002 AmmoniaEmission Inventory for the Continental United States, using 2007 cropping patterns and fertilizer applicationrates. A process-based ammonia emissions model developed for LADCO [R. Zhang et al., 2005; Mansell et al.,2005] was then used to develop monthly and hourly temporal allocation factors. Area and point sources inthe Upper Midwest were based on data supplied by states. For other states in the modeling domain, 2005emissions were used, as provided by other regional planning organizations. Emissions for Canada were basedon the 2005 Canadian National Pollutant Release Inventory, v.1.0 (NPRI). Canadian area sources wereallocated from provincial totals to the modeling grid proportionally to gridded population.

Hourly biogenic emissions from the Model of Emissions of Gases and Aerosols from Nature (MEGAN) v.2.04[Guenther et al., 2006] were processed to both grids from hourly WRF meteorology and speciated for CB05 asin Wilkinson [2006].2.1.4. Process AnalysisThe two techniques available in process analysis are the integrated process rate (IPR) analysis and integratedreaction rate analysis (IRR) [Byun and Ching, 1999]. IPR quantifies the net mass changes for each modeledgas and aerosol species due to calls to the model process subroutines—horizontal and vertical advectionand diffusion; gas phase chemistry; aerosol module; cloud module (including wet deposition and aqueouschemistry); direct (primary) emissions; and dry deposition. Values are calculated for each model grid celland averaging time (in this case, 1 h). To streamline analysis, horizontal advection and diffusion werecombined into one horizontal transport term and vertical advection and diffusion were combined intovertical transport.

While IPR provides insight on the contribution of different model processes, IRR provides a detailedquantification of individual reaction rates within the gas phase chemistry module. Specifically, thecontribution of each reaction to the concentration of each species can be tracked using IRR. By followingindividual reactions within reaction pathways, contributions to overall nitrate formation were determined.

The daytime HNO3 formation pathway consists of reaction (R1):

NO2 g" # $ OH g" # ! HNO3 g" # (R1)

Heterogeneous hydrolysis of N2O5 on and within aqueous aerosol particles (R2) has been identified as animportant N2O5 sink during the night [Platt et al., 1984; Mozurkewich and Calvert, 1988].

N2O5 g" # $ H2O l" # ! 2HNO3 aq" # (R2)

Homogenous reaction of N2O5 with water vapor (R3) and (R4) also occurs but is thought to be veryslow [Tuazon et al., 1983; Wahner et al., 1998] and therefore minor compared to R2 under typical



tropospheric conditions [Russell et al., 1985; Dentener and Crutzen, 1993; Hanway and Tao, 1998; Changet al., 2011]:

N2O5 g" # $ H2O g" # ! 2HNO3 g" # (R3)

N2O5 g" # $ 2H2O g" #!2HNO3 aq" # $ H2O g" # (R4)

Both (R3) and (R4) are implemented in the CMAQ 4.7.1/CB05 model configuration of this study.

Reaction (R2) is implemented in CMAQ as a pseudo-first-order reaction, equation (1) [Heikes and Thompson,1983; Chang et al., 1987]

d N2O5% &dt

!!!!het

' !kN2O5 N2O5% & (1)

where d[N2O5]/dt is the loss rate of N2O5 due to reaction (R2), [N2O5] is the N2O5 mixing ratio, and kN2O5 is therate constant parameterized as follows:

kN2O5 ' 14CN2O5 (γN2O5

(S (2)

where CN2O5 is the meanmolecular velocity of N2O5, γN2O5 is N2O5 heterogeneous reaction probability, and Sis the aerosol surface area density. HNO3 produced by R2 can partition between aerosol and gas phase toestablish thermodynamic equilibrium [Chang et al., 2011]. In CMAQ, R2 is implemented in the HETCHEMsubroutine, which adds the heterogeneously produced HNO3 to the gas phase. This is then subsequentlypartitioned by ISORROPIA before any transport, reaction, or deposition routines are called.

2.2. Analytical Framework2.2.1. Episode and Day/Nighttime DefinitionEach hour of the modeling period was classified into one of two categories: episode or non-episode. Withepisode periods we specifically refer to the periods of nitrate accumulation, as we are focused on the processesthat lead to accumulation of TNO3 and aerosol nitrate, and not on the processes that are present during the hoursof the highest pollutant concentrations. Thus, the categorization was performed as follows: (1) the modeledaerosol NO3

! time series was smoothed by calculating a 7hmoving average; (2) hours with an increase in aerosolnitrate concentrations 0.053μgm!3 h!1 or higher (the average rate during the representative 24–29 JanuaryPM2.5 event) were tentatively placed in the episode category; (3) hours within 4h of the hour of peak modeledaerosol nitrate concentration were excluded from the episode period. The definition of the “episode”classification deliberately excluded the hours of peak concentration to avoid hours with large horizontaltransport fluxes from the synoptic air mass changes that mark the end of many air quality episodes.

The dataset was further divided into daytime and nighttime periods. Daytime was defined as 08:00 to 19:00local standard time (LST), and nighttime as 19:00 to 08:00 LST. Sunrise ranged from 07:23 (1 January) to 05:36(31 March). Sunset times ranged from 16:30 (1 January) to 18:16 (31 March).2.2.2. Spatial and Vertical AveragingIn our analysis we report vertically integrated areal rates of processes and reactions. The vertical integrationincluded CMAQ layers 1–20 (0 to ~3 km). In some cases, these areal rates were further horizontally averagedover the modeled 800,000 km2 region of peak wintertime TNO3 concentrations, centered over northwesternIndiana, including Illinois, Indiana, and Ohio and encompassing portions of 9 other states and southern Ontario(Figures 1a and 1b). Areal rates are stated in units of μmole/m2-day.2.2.3. Flux and Reservoir Showing the Cycling of NOy

For further visualizing NOy and ammonia cycling, flux and reservoir diagrams were developed. Six species orspecies families were considered for four vertical layers, for a total of 24 reservoirs. The six species or speciesfamilies were gas phase ammonia, aerosol fine nitrate, nitric acid, N2O5, other NOy (NO3, HONO, PAN, PANX,PNA, and NTR), and NOx. The four layers include CMAQ layer 1 (0–20m), layers 2–4 (20–106m), layers 5–9(106–441m), and layers 10–12 (441–1000m). The fluxes between reservoirs were calculated using both IPR andIRR rates as appropriate. Thirty-five non-negligible chemical reactions were identified as important fortransferring NOy species between the various reservoirs. These were further simplified into six reaction groups.All reaction rates were converted to a basis of moles nitrogen (moles N). The mapping of reactions to thereaction groups is shown in Table 1. Reaction (e) in Table 1 (heterogeneous formation of HNO3 on aerosols) wascalculated in the aerosol module rather than in the gas phase chemistry module where IRR tracking occurs.More specifically, reaction (e) was calculated from the effect of the aerosol module process on N2O5, and equal



to twice the disappearance rate ofN2O5 in the aerosol module(converted to molar units). Aerosolchemical production of nitrate, wetdeposition of HNO3 and aerosolnitrate, and vertical redistribution ofHNO3 and aerosol nitrate inconvective clouds were tracked butare not shown in the diagrams.Changes in coarse mode aerosolnitrate (which are calculated in theaerosol process) were not trackedby IPR but were rather calculated bydifference based on a mass balanceof nitrate in the aerosol module.

3. Results and Discussion3.1. Chemical TransportModel Evaluation

This section summarizes modelperformance for PM2.5 mass andinorganic ions at Mayville andMilwaukee, and at monitors fromthe EPA Speciation Trends Network(STN) and Interagency Monitoringof Protected Visual Environments(IMPROVE) network in the regionalmodeling domain. Extensivemodel-observation comparisonand performance statistics ofrelevant quantities, includingmeteorology, O3, and gas-phaseprecursors, can be found in Spaket al. [2012]. Table 2 summarizesmodel bias, error, root-mean-square error (RMSE), fractional bias(FB), fractional error (FE), andcoefficient of determination (r2).Performance metrics at the twoLADCO WNS observation sites fallinto similar ranges for everyevaluation metric. Mean aerosolburdens at Milwaukee are allbiased low, while Mayville only

exhibits a low bias for sulfate, and model evaluation at the IMPROVE and STN stations features consistentpositive biases for aerosols. In all cases, mean biases are <13% of observed mean concentrations at the twosites. Overall, performance for PM2.5, O3, and speciated inorganic ions meet or exceed community standardsfor chemical transport modeling [Morris et al., 2005] and recent performance evaluations for regulatory andresearch modeling [Simon et al., 2012] throughout the study, with the exception of NO3

! at the (mostlyurban) STN sites, where high fractional error limits performance (supplemental information, Table S1). Amongprecursor gases, NOx, NOy, NH3, and HNO3 exhibit negative biases, while SO2 is overpredicted and modeledO3 is close to observations at both sites (Table S2). Model meteorological performance (Table S3) shows

Table 1. Reactions Used to Calculate Chemical Fluxes

ReactionGroupa Reactionb

a(+) O + NO2! NO3NO2 +O3! NO3

a(!) NO3 !hν NO2 $O

NO3 !hν NONO3 + NO ! 2 (NO2

NO3 + NO2!NO + NO2b(+) NO2 +OH ! HNO3c(+) NO3 + NO2! N2O5c(!) N2O5 !NO3 + NO2

N2O5 !hν NO2 $ NO3

d(+) N2O5 + H2O 2 (HNO3N2O5 + H2O + H2O ! 2 (HNO3

e 2 times the rate of change of N2O5 concentration due to the AEROprocess in integrated process rate (IPR)

f(+) NO + NO2 + H2O! 2 (HONONO +OH ! HONOHO2 + NO2! PNAXO2N + NO ! NTRC2O3 + NO2! PANCXO3 + NO2! PANXROR + NO2! NTR

CRES + NO3! CRO + HNO3CRO + NO2 ! NTR

f(!) HONO !hν NO $ OHOH + HONO ! NO2

HONO+ HONO ! NO + NO2PNA ! HO2 + NO2OH + PNA ! NO2

PNA !hν 0:61(HNO2 $ 0:61(NO2+ 0.39 (OH + 0.39 (NO3PNA ! C2O3 + NO2PNA ! C2O3 + NO2PANX ! CXO3 + NO2PANX ! CXO3 + NO2

PANX + OH ! ALD2 + NO2g(+) NO3 + HO2 ! HNO3

ALD2 + NO3 ! C2O3 + HNO3ALDX + NO3 ! CXO3 + HNO3

a(+) indicates formation and (!) indicates destruction. Net rates are theformation minus destruction terms. (a) net NO3 radical formation from NOx;(b) HNO3 from OH oxidation of NO2 (R1); (c) net N2O5 formation; (d) homo-genous formation of HNO3 from N2O5 (R3 + R4); (e) heterogeneous conver-sion of N2O5 to NO3 (R2); (f ) net formation of other NOy from NOx; (g)HNO3 formation from the NO3 radical.bSpecies abbreviations are as follows: PNA = peroxynitric acid (HNO4),XO2N= organic nitrate from NO+ RO2, NTR = organic nitrate (RNO3),PAN=peroxyacetyl nitrate; C2O3 = acetylperoxy radical; CXO3 = C3 and higheracylperoxy radicals; PANX = C3 and higher peroxyacyl nitrates;ROR = secondary alkoxy radical, CRES = cresol and higher molecularweight phenols, CRO=methylphenoxy radical, ALD2 = acetaldehyde; andALDX= propionaldehyde and higher aldehydes.



results consistent with contemporary modeling for chemical transport, with relative humidity (temporalvariability) and wind speed (positive bias) as limiting factors in simulating chemical transport.

Observations showed that during episodes, fractional aerosol composition shifted toward a higherammonium nitrate fraction, partly through secondary aerosol production, and partly from enhancedpartitioning to the aerosol phase [Stanier et al., 2012]. Concentrations of all aerosol species increased duringepisodes, but the observed relative increase was greatest for ammonium and nitrate. The increase inammonium nitrate, the ammonium nitrate diurnal pattern, and the increase in partitioning of total nitrate tothe aerosol phase were all reproduced by the model. Average diurnal profiles of modeled and measuredNO3

! can be found in the supplemental material, with additional diurnal profiles in Spak et al. [2012]. Themodeled aerosol nitrate fraction (fine aerosol nitrate/fine aerosol nitrate +HNO3) slightly increased fromnon-episode to episode conditions (0.84 to 0.86, and 0.95 to 0.96, respectively, at Milwaukee and Mayville).While these features of the measured episodes were reproduced, room for improvement in model skill does

Table 2. CMAQ Performance Statistics for PM2.5 and Inorganic Ions From Hourly Observations at Milwaukee, Mayville,and All STN and IMPROVE Sites in the Regional Modeling Domaina

Milwaukee Mayville STN IMPROVE

PM2.5Number of observations 2118 2124 1385 853Mean observed (μg/m3) 17.2 11.69 12.10 6.96Mean modeled (μg/m3) 15.9 12.35 15.60 8.64r2 0.34 0.30 0.25 0.41Fractional bias !0.12 0.12 0.12 0.12Fractional error 0.46 0.54 0.53 0.46MB (μg/m3) !1.29 0.66 3.50 1.64ME (μg/m3) 7.44 5.9 7.94 3.69RMSE 10.91 8.07 12.03 5.39

NO3!

Number of observations 28 27 1382Mean observed (μg/m3) 4.35 4.58 3.00Mean modeled (μg/m3) 3.96 4.78 3.02r2 0.33 0.29 0.41Fractional bias !0.10 0.01 !.022Fractional error 0.72 0.68 0.82MB (μg/m3) !0.37 0.19 0.03ME (μg/m3) 2.67 2.92 2.19RMSE 4.15 4.12 2.38

NH4+

Number of observations 28 27 1382Mean observed (μg/m3) 2.13 2.06 1.66Mean modeled (μg/m3) 1.86 2.10 1.96r2 0.42 0.43 0.36Fractional bias !0.08 0.16 0.05Fractional error 0.46 0.50 0.57MB (μg/m3) !0.24 0.09 0.40ME (μg/m3) 0.95 1.01 1.06RMSE 1.46 1.45 1.03

SO4!

Number of observations 28 27 1382 853Mean observed (μg/m3) 2.03 2.21 2.35 1.77Mean modeled (μg/m3) 1.95 1.87 3.58 2.57r2 0.19 0.24 0.13 0.46Fractional bias 0.04 !0.06 0.26 0.29Fractional error 0.62 0.64 0.51 0.48MB (μg/m3) 0.03 !0.28 1.23 0.79ME (μg/m3) 1.27 1.28 1.83 1.13RMSE 1.95 1.88 3.15 1.78aAll comparisons are for 24 h integrated samples except hourly Milwaukee and Mayville PM2.5.



exist. The model generally underpredicted episode fine ammonium nitrate concentrations during observedepisodes. Furthermore, the model did predict some periods of elevated ammonium nitrate concentrationsthat were not observed in the field study, or that were observed but at lower concentrations than predicted inthe model. The underprediction of NH3 in Eastern Wisconsin was noteworthy, with mean modeled values of0.5–0.9 ppb and mean observed values of 2.3–2.4ppb at the two intensively monitored sites. Performance forammonia improved during the episode periods, particularly at the rural site.

An additional variable useful for contextualizing model output is the gas ratio [Ansari and Pandis, 1998],

Gas Ratio ' free ammonia under assumption of full neutralizationtotal nitrate

' TA! 2TSTN

(3)

where TA is total ammonia, TS is total sulfate, and TN is total nitrate (all in molar concentrations). High gasratios indicate high ammonia availability. Ammonia availability is critical to TNO3 accumulation because ofthe short dry deposition lifetime of gas phase HNO3 compared to that of aerosol NO3

!. Model-observationcomparison showed some negative bias in gas ratio on average at the Mayville and Milwaukee sites; this biaswas more severe during observed non-episode periods. During observed episodes, the urban gas ratioaveraged 1.6 (observations) vs. 1.1 (model). At the rural location, the mean bias was lower, with gas ratios of1.2 (observations) and 1.3 (model). Gas ratios varied significantly across the domain, with ammonia rich areas(GR> 1) in the western half of the region and a consistently ammonia-poor area near the Ohio River.Ammonia availability tended to decrease during air quality episodes in both observations and model.

3.2. Attribution of Total Nitrate Formation to Pathways

Table 3 summarizes, at the two LADCO measurement sites, a selection of model variables relevant to aerosolnitrate episodes. The modeled aerosol nitrate time series at the two LADCO WNS measurement locations areshown in Figure 2, with periods of aerosol nitrate concentration increase highlighted. As stated in themethods section, the nomenclature “episode” will be used to refer to these periods, although a more precisename would be “aerosol nitrate concentration increase periods”.

To assess relative contributions to the TNO3 chemical pathways, we examined the relative amounts thedaytime pathway (R1) vs. the nighttime pathway (R2 + R3 + R4), as well as the heterogeneous andhomogeneous contributions to the nighttime pathway. For this analysis, we assumed the sum of the rates R1to R4 as the TNO3 formation rate, as they accounted for over 98% of all chemical formation of TNO3 onaverage in themodel, and over 95% of total TNO3 formation rate during the daytime. The largest of theminorpathways making up the remaining 2 to 5% TNO3 production were reactions involving NO3 radicalconversion to HNO3 (see group g in Table 1).

Tables 4 and Figure 3 show areal TNO3 formation in atmospheric columns from the ground to the top of modellayer 20 (approximately 3 km above ground). Results in Table 4 and Figure 3 are for the 800,000 km2 high-nitrateproduction subdomain described in the methods section and mapped in Figure 1b. The grand average TNO3

formation rate over the 3month model period is 108 μmole/(m2-day), with 72% of TNO3 formation occurring

Figure 2. Modeled fine aerosol nitrate concentrations at the two LADCOwinter nitrate study locations and indication of thetime periods meeting the criteria for episode buildup periods.



via the nighttime pathway. The fraction attributable to the nighttime pathway remains high in all months,with a maximum of 81% in January and a minimum of 65% in March. TNO3 production is lowest in January, at101μmole/(m2-day), but the difference between the highest production month (March, 119μmole/(m2-day))and January is modest. Contributions by homogeneous reactions of N2O5 to the nighttime pathway are notnegligible; for example, they constitute 28% of the nighttime pathway and 15% of overall TNO3 formation inMarch. The relative contribution of the heterogeneous pathway to TNO3 formation generally decreases withseasonal change from winter to spring [Asaf et al., 2010; Han and Song, 2012]. In a model study for continentalEurope (averaged over 10°W to 30°E), the ratio is diminished from ~0.7 in December to ~0.5 in March and~0.2 in July at the same latitude as Milwaukee (42°W) [Schaap et al., 2004].

For comparison, the model has (averaged over this same 800,000 km2 area), an NO emission rate of296μmole/m2-day, an NO2 emission rate 21μmole/m2-day, and a total NOx emission rate of 317μmole/m2-day.The mean reactive conversion fromNOx to NOz within the area (and within the 0–3 km height limit) is thus 34%of the emissions.

Figure 3 shows the temporal variation in the areal TNO3 formation rate. High TNO3 chemical formation ratesusually correspond to aerosol nitrate accumulation episodes (yellow shading), but occasionally transport alsoinfluences accumulation. The high degree of variability on daily and synoptic timescales in formation rates ismainly from variation in reactant concentrations. The most apparent month-to-month trend is the increase inthe daytime pathway. The month-to-month trends in both of the nighttime pathways (Table 4) are smallcompared to synoptic variability.

The doubling in production via the daytime pathway from January to March, 19 to 41μmole/(m2-day), ispredominantly due to increased OH abundance for R1 (NO2 +OH). The 57% increase in the homogeneousnighttime pathway (R3 + R4), 14 to 22μmole/(m2-day), is due to increases in the concentrations of the

Table 4. Column Total Nitrate Formation Rates Averaged Temporally for Individual Grid Cells Over City Centers and Spatially Over the High Nitrate Subdomain

LocationTemporalAveraging

Daytime Pathway(μmole/m2-day)

Nighttime Pathway(μmole/m2-day)

NighttimePathway/Total

HomogeneousNighttime Pathway(μmole/m2-day)

HeterogeneousNighttime Pathway(μmole/m2-day)

HomogeneousNighttime Pathway/Nighttime Pathway

High nitratesubdomain average

Jan 18.8 81.9 0.81 13.5 68.4 0.17Feb 29.3 75.6 0.72 14.6 60.9 0.19Mar 41.3 77.2 0.65 21.8 55.4 0.28

Jan–Mar 29.8 78.3 0.72 16.7 61.6 0.21Milwaukee Jan–Mar 35.6 67.0 0.65 14.6 52.4 0.28Mayville Jan–Mar 21.5 62.9 0.75 12.7 50.2 0.25Detroit Jan–Mar 38.2 89.6 0.70 16.7 72.9 0.23

Table 3. Modeled Concentrations at Milwaukee and Mayville

Parameter

Milwaukee Mayville

Overall Episode Overall Episode

PM2.5 mass (μg/m3) 15.9 20.0 12.4 16.1Total nitrate (μg/m3) 4.9 6.4 5.6 8.1Aerosol nitrate (μg/m3) 4.1 5.5 5.3 7.7Nitric acid(g) (μg/m3) 0.8 0.9 0.3 0.3Aerosol nitrate fractiona (%) 83.7 85.8 94.5 95.7Total ammonium (μg/m3) 2.3 2.8 3.0 4.0Ammonia(g) (μg/m3) 0.4 0.4 0.6 0.8Aerosol ammonium (μg/m3) 1.9 2.4 2.4 3.2Aerosol ammonium fractionb (%) 84.0 85.1 78.6 79.6Gas ratio (dimensionless) 1.1 1.1 1.4 1.3Temperature (°C) !0.9 !0.8 !5.9 !5.8RH (%) 66.4 65.7 85.9 87.5Total aerosol surface area (μm2/cm3) 162.5 201.0 125.1 158.1

aPM2.5 aerosol nitrate (NO3!) divided by PM2.5 NO3

! + HNO3(g).bPM2.5 aerosol ammonium (NH4+) divided by PM2.5 NH4

+ + NH3(g).



reactants (e.g., water vapor, NO2, NO, and NO3 radical). These results are based on results from stepwiseregression to explore the impact of 15 potential explanatory variables for reproducing the day-to-day variabilityof the daytime, nighttime heterogeneous, and nighttime homogeneous pathways. Correlation among variables(e.g., many variables are increasing similarly from winter to spring, such as OH, temperature, NH3, HO2, watervapor, etc.) and nonlinearity limit the power of this type of investigation; however, we constructed linearregression of groups of four to six statistically significant (p< 0.05) explanatory variables. For example, dailyhomogeneous TNO3 production could be modeled with explanatory variables of water vapor, NO3, N2O5, andNO in layer 6 with r=0.962 and p< 0.02 for each explanatory variable. The stepwise regression yielded linearregression coefficients for aerosol surface area and γN2O5 consistent with the competition between theheterogeneous and homogeneous pathways for reaction with N2O5. Aerosol surface area and γN2O5, whenincluded in linear models for the heterogeneous pathways, routinely had positive regression coefficients.Conversely, if included in regression models for the homogeneous pathway, negative coefficients werecalculated. The slight decrease in the heterogeneous nighttime pathway (R2), from 68 to 55μmole/(m2-day),has a variety of causes, including changes in aerosol loadings, increases in the competitiveness of thehomogeneous pathway, and changes in NOx, NO3, and N2O5 concentration as the season progresses.

As explained in the introduction, two important features demonstrated in laboratory and field studies withrespect to the heterogeneous nighttime pathway are the formation of ClNO2 (instead of HNO3) from thereaction of N2O5 with dissolved chloride to form ClNO2, and the lowering of γN2O5 due to organic particlesand organic coatings.

From examination of the results of Sarwar et al. [2012], we can estimate the error in our TNO3 formation ratescaused by neglecting the reaction of N2O5 with dissolved chloride to form ClNO2. Although the effects ofchloride on the NOy system are strongest in coastal areas, chloride concentrations are sufficient to make theClNO2 chemistry non-negligible in the wintertime Midwest. The reaction diverts nitrogen from TNO3

formation and thus reduces NO3! and HNO3 concentrations. Sarwar et al. modeled February 2006 and

published maps of the reduction in TNO3 from the ClNO2 pathway as well as time series and vertical profilesfor the average of a group of grid cells in the peak TNO3 area in Indiana. Sarwar et al. showed a reduction innighttime TNO3 of up to 0.6μg/m3 and formation of up to 0.5 ppb of ClNO2. ClNO2 concentrations werecomparable to N2O5. Mean nighttime TNO3 reductions at the surface were ~0.1 and ~0.2μg/m3 in IndianaandWisconsin, respectively. The percentage reductions in peak TNO3 (Indiana) were about 6%, and themeanreductions in Wisconsin and Indiana were between 1 and 3%.

Modeled surface chloride concentration in Indiana was ~0.04μg/m3 [Sarwar et al., 2012]. This value is similar tothosemeasured in the IMPROVE network for BlueMounds, MI (0.061μg/m3), and Bondville, IL (0.042μg/m3), forFebruary 2013. Inspection of IMPROVE chloride time series at Midwest sites indicates seasonal maxima inwintertime and a strongly skewed distribution with high peak values that may be related to the timing of roadsalt application and the subsequent meteorological conditions afterward. We conclude that the error in theupper Midwest due to neglecting ClNO2 formation was probably less than 6% (in terms of surface massconcentration of TNO3) but that further study is warranted, especially since the uneven spatial and temporalpatterns of road salt application may have an interaction with the meteorology of some wintertime episodes.

Figure 3. Twenty-four-hour running average areal HNO3 formation rates from each pathway, integrated spatially over thehigh nitrate formation subdomain.



Organic coatings and particles with high organic fraction have been shown to have much lower γN2O5 valuesthan corresponding organic-free systems, and this has been proposed as a key reason for the discrepancybetween modeled γN2O5 and corresponding values inferred from field measurements [Brown et al., 2006].This effect is particularly important at reducing high γN2O5 found in low nitrate conditions [Riemer et al., 2009].Comparing our relative humidity values (75–92%) and our OC to sulfate ratios (0.8 and 1.2 in model andobservations, respectively, in Milwaukee; 0.5 and 0.8 in model and observations, respectively, in Mayville) tothe values in Figure 1 of Bertram et al. [2009], we expect that the suppression of γN2O5 due to organics ismodest for our conditions. Instead, NO3

! and RH should be the factors controlling γN2O5. In summary, thehigh RH values during Midwestern episodes (75 and 92% for mean episode RH at Milwaukee and Mayville,respectively) together with the high nitrate fraction, which is already suppressing γN2O5, and the lowcontribution of secondary organics [Stanier et al., 2012], let us conclude that neglecting the effects of organiccoatings on γN2O5 will likely have a small impact on the simulation of wintertime episodes in the upperMidwest. Within the upper Midwest during episodes, the OC fraction is highest in populated areas—andthe heterogeneous nighttime pathway rate is likely overestimated by the current model in those locations.Maps of γN2O5 are included in a supplement of the paper (Figure S2).

3.3. Diurnal Patterns in HNO3 Formation Rates

Modeled average diurnal patterns for the daytime and nighttime pathways are plotted for Milwaukee andMayville in Figure 4. Figure 4a (Milwaukee) is representative of an urban area in the high nitrate concentrationregion, while Figure 4b (Mayville) represents a rural area. The formation rate within the surface layer is shown,although similar patterns are found throughout the boundary layer. The daytime pathway rates peak at 14:00at both sites. During the model periods identified as episodes the daytime rate is somewhat elevated.The surface-level nighttime pathway for the rural location is much stronger than the urban location while thesurface-level daytime pathway is weaker (Figure 4). This may appear inconsistent with Table 4, where thenighttime pathways are roughly of the same strength for both locations. The difference is that Table 4shows the column-integrated rate (summed over 20 model layers), while Figure 4 refers for the surface only.Vertical profiles are explored in more detail below.

3.4. Spatial Distributions of TNO3 Formation Rates During Episodes

Maps of TNO3 formation rates, integrated over the lower troposphere (layer 1–20, 0 to ~3 km), show spatialvariability during episode periods. Figure 5 maps the daytime pathway rate (Figure 5a), the nighttimepathway rate (Figure 5b), the sum of daytime and nighttime pathway rates (Figure 5c), the fraction of totalformation attributed to the nighttime pathway rate (Figure 5d), the fraction of nighttime formation attributedto the homogeneous pathway rate (Figure 5e), and the surface ozone concentration averaged during theseperiods (Figure 5f).

TNO3 production (Figure 5c) has a broad regional background, with production rates of greater than100μmole/m2-day over most of the domain. Within the broad area of elevated TNO3 formation, two regionsstand out: the Ohio River Valley and the southern Great Lakes. Urban enhancements in the daytime formation

(a) (b)

Nighttime pathway for episodesDaytime pathway for episodesNighttime pathway period averageDaytime pathway period average

Figure 4. Diurnal cycle of the pathways for TNO3 formation of the surface at (a) Milwaukee and (b) Mayville.



pathway in northern urban areas largely cancel out urban deficits in the nighttime pathway, leading to TNO3

production maps that do not have strong signatures from urban centers.

The daytime formation pathway (Figure 5a) shows high rates in the Ohio River Valley, and localized urbanhotspots due to high urban NOx, mainly from transportation sources. Vertical profiles (Figure 7) demonstratethat urban HNO3 formation is primarily a surface phenomenon.

The nighttime pathway (Figure 5b for the absolute total and Figure 5d for the fraction) is the predominantpathway (greater than 50% of the total) throughout the entire domain, and is most important on a fractionalbasis in the northern portions of the domain and over the Great Lakes. The broad features of the nighttimepathway rate during episodes (Figure 5b) can be explained by limited aerosol surface area, water vapor, andNOx to the west of the Mississippi river and to the north of the areas of highest TNO3 production. To thesoutheast, lower NOx and higher temperatures limit TNO3 production via the nighttime pathway.

The areas of high nighttime rates (the Ohio River Valley and the southern and western portions of LakesMichigan and Erie) have slightly different explanations. High NO2 concentrations, 7–20 ppb overall averagedat the surface, are seen at both of these locations and contribute to enhanced HNO3 formation. Remotesensing retrievals from the Ozone Monitoring Instrument (OMI) confirm high NO2 column density(8–10! 1015molecules cm!2) over the Ohio River Valley and the southern Great Lakes for averagewintertime conditions (October–March 2005) [Russell et al., 2012].

Over the Great Lakes, several factors favor TNO3 production. The air temperature during episodes at nightover the Lakes is higher than over the surrounding land. In particular, the temperature in the model surfacelayer, where peak conversion from N2O5 to TNO3 occurs, is 2°C over the central Lake Michigan, compared to!6°C over Mayville, Wisconsin. This leads to an increased reaction rate for NO3 formation via the reaction of

(c)

(e)

(d)

(f)

(b)MayvilleMilwaukee

mol

e/m

2 ·da

y

mol

e/m

2 ·da

y

mol

e/m

2 ·da

y

64

56

48

40

32

24

16

8

0

180

160

140

120

80

60

40

20

0

ppb

40

35

30

25

20

15

10

20

0

0.40

0.35

0.30

0.25

0.20

0.15

0.10

210

175

140

105

70

35

0

0.85

0.75

0.70

0.65

0.60

0.55

0.50

0.80

(a)

Figure 5. Spatial distribution of vertical column total HNO3 (TNO3) formation rate and surface ozone concentration duringthe episodes. (a) Daytime pathway rate, (b) nighttime pathway rate, (c) sum of daytime and nighttime pathway rate, (d)ratio of nighttime pathway rate to total formation rate, (e) ratio of homogeneous nighttime pathway rate to nighttimeformation rate, and (f ) ozone concentration at the surface. Note different scales.



NO2 and O3 (about a factor of 1.3 for the 8°C temperature difference). NO3 formation is also increasedbecause the concentrations of precursor O3 are about 1.5 times higher over the Lakes than over centralWisconsin. Moreover, the nocturnal boundary layer is about two times deeper over the Lakes, which allowsmore water to collect in the atmosphere, and the deeper boundary layer decreases the probability thatreactive intermediates are lost by deposition at the surface. Moreover, deposition velocities over watersurfaces are lower than over land, and the values for γN2O5 over the Lakes are slightly increased.

While the temperature differential between Lakes and surrounding land increases the production of NO3,and hence favors TNO3 production, it also changes the equilibrium constant for N2O5 by a factor of about 3.3,assuming equilibrium parameters from Chang et al. [2011]. This in turn shifts the equilibrium toward NO3 andtends to reduce TNO3 production. Figure 5b shows that, overall, the increases in TNO3 production exceed thedecreases, and Figure 5d shows that over Lakes Michigan and Huron, but not over Lake Erie, much of theincrease is from the homogeneous nighttime pathway.

The Ohio River Valley has high daytime (Figure 5a) and nighttime (Figure 5b) production rates, whichcombine to give very high total production rates relative to the other areas of the domain. High NO2 levels, asdiscussed above, are certainly important. Other factors that are characteristic of the high TNO3 formationrates in the Ohio River Valley are: acidic condition with high sulfate (and thus high γN2O5), high aerosol surfaceareas, and high O3 and air temperatures (which make for favorable NO3 formation) compared to the rest ofthe domain.

Within the area of high nighttime TNO3 production, there are some subtle spatial variations. For example,there are local minima in the rate in Chicago, near Cleveland, near Detroit, east central Indiana, and west-central Ohio. These spatial variations are partly explained by low nocturnal O3 concentrations due to titrationby NO. This lack of nocturnal O3 inhibits NO3 formation (NO2 +O3!NO3+O2), as NO3 is required forsubsequent N2O5 formation in the nighttime pathways. NO can also suppress NO3 concentrations via thereaction NO3+NO! 2NO2. While there are broad similarities in spatial pattern of O3 locations (Figure 5f) andnighttime rates (Figure 5b), there are also key differences. For example, Cleveland and Pittsburgh both havelow O3 concentrations, but only Cleveland has a local minimum in the nighttime production rate. Given themultiple reaction steps and the complex vertical profiles of the reactants, such counterexamples are notsurprising. Future work should explore these spatial patterns more fully, and investigate potential parallelswith NOx influence of urban-rural gradients to summertime ozone [e.g., Grabow et al. [2012]].

3.5. Horizontal and Vertical Concentration Distribution During Episodes

While sections 3.2–3.4 focused on the spatial patterns of the TNO3 formation, this section addresses patternsin the reactants (we focus here on N2O5) and the products (total nitrate, aerosol nitrate, and nitric acid).Figure 6 maps total nitrate (Figure 6a), fine aerosol nitrate (Figure 6b), the fine aerosol to total nitrate ratio(Figure 6c), nitric acid (Figure 6d), and N2O5 (Figure 6e). See section 2.1.2 for treatment of modes incalculating total and aerosol nitrate. Each figure includes a map of the species for the entire model run (left),for daytime hours during episodes (middle), and for nighttime hours during episodes (right). Horizontaldistribution plots are shown at the surface and near the elevation of peak HNO3 and N2O5 (model layer 6).Curtain plots show concentrations as a function of elevation at the latitude of Milwaukee for each species.At the latitude of Milwaukee, several distinct airsheds are shown in the curtain plots. These include (fromwest to east), southern Wisconsin (including Milwaukee), southern Lake Michigan, central Michigan(including the urban airsheds Holland/Grand Rapids, Lansing, Detroit/Windsor), and Lake Erie.

Elevated total nitrate, fine aerosol nitrate and nitric acid in Upper Midwest have their highest values at night(Figures 6a–6c), with broad regions ranging from 6μg/m3 to 9.5μg/m3 TNO3 at the surface. However, lack offree ammonia significantly lowers modeled NO3

! concentration in the southern region of the Ohio RiverValley. TNO3 concentrations in excess of 5μg/m3 are modeled to only occur at elevations below about 500m.Episode nighttime TNO3 concentrations typically exceed episode daytime values by 1–2μg/m3.

High aerosol nitrate (Figure 6b) at the surface during episodes appears throughout the Midwest over land,but not over the Great Lakes. Higher temperatures and lower ammonia availability over the Great Lakes causenitrate there to exist predominantly in the gas phase.



The aerosol nitrate fraction (Figure 6c) has a strong vertical gradient, with the aerosol phase favored at thesurface. In northern urban areas, local ammonia emissions likely contribute to local enhancement of theaerosol nitrate fraction. This is most apparent in the aerosol nitrate fraction curtain plot, where the centralMichigan urban airsheds (Holland/Grand Rapids, Lansing, and Detroit/Windsor) can be clearly distinguished.Conventional thinking about the aerosol nitrate fraction [Wittig et al., 2004] suggests a strong peak inthe aerosol fraction in the early morning driven by low temperature and high relative humidity, and aminimum in the aerosol fraction in the afternoon driven by gas phase nitric acid production, hightemperature, and low relative humidity). Contrary to this hypothesis, there is not a big difference betweensimulated daytime and nighttime fractions during episodes. This similarity in production during episodes,despite differences in the meteorological and chemical environment, is caused by high nitric acidproduction during the night and temperatures and relative humidities sufficient to support aerosolpartitioning during both day and night, especially in the northern parts of the region. The aerosol fractiondistributions correspond strongly to free ammonia, and the variability is associated with variability inammonia emissions. Maps of gas ratio and a plot of nitrate fraction vs. gas ratio (r= 0.72) are included as asupplement (Figures S3 and S7).

Due to short lifetimes for nitric acid and some of its precursors (N2O5, NO3 radical) with respect to deposition,as well as to ammonia gradients which favor particulate nitrate at the surface and nitric acid aloft, nitric acid(Figure 6d) is depleted in the surface layer and has peak concentrations from 200 to 500m above the ground.High concentrations of nitric acid extend horizontally over a broad region with maximum concentrationslocated near the Ohio River Valley. During episodes, the area of high nitric acid concentration (in model layer6) extends to Illinois and Michigan during nighttime. This change in the shape of the high HNO3 area is likely

(a)

(b)

(c)

(e)

(d)

Figure 6. Spatial distribution and vertical cross section at the latitude of Milwaukee for (a) total nitrate, (b) aerosol nitrate, (c) aerosol nitrate fraction, (d) nitric acid,and (e) N2O5. Columns are for overall conditions (left), daytime during episodes (middle), and nighttime episodes (right). Red dotted line on (a) indicates transect forvertical cross sections.



an artifact of the episode definition, which was based on the rate of change of nitrate concentrations insoutheastern Wisconsin.

Mixing ratios of N2O5 are clearly enhanced (Figure 6e) in layers from 100m to 500m during the night. Weakvertical mixing in the nighttime boundary layer leads to titration of O3 near the surface by NO, and NO3

concentrations are further decreased by reaction of NO3 with NO and VOC. Coupled with deposition of NO3

and N2O5 at the surface, this O3 titration leads to low concentrations of N2O5 at the surface [Riemer et al.,2003; Geyer and Stutz, 2004]. The largest mixing ratios of N2O5 are found aloft at night and cover LakeMichigan and the lower peninsula of Michigan. Over the Lake, the N2O5 layer reaches higher elevations dueto higher boundary layer heights.

N2O5 concentrations are determined by both sources (NO3 +NO2) and sinks (heterogeneous andhomogeneous reactions, deposition, thermal dissociation) of N2O5, and low concentrations may reflecteffective sinks rather than weak sources. N2O5 deficits seen at urban locations are attributed to lowproduction (due to NO titration of O3 and consequently low NO3 concentrations, as discussed above),as well as to rapid reaction on locally enhanced aerosol concentrations and rapid deposition. Althoughdeposition velocities are dependent on many land cover and meteorological variables, inspection of HNO3,N2O5, and NO3 deposition velocity maps (included as a supplemental section) indicates higher rates in some,but not all urban areas. Enhanced deposition of HNO3 in urban areas due to surface roughness andturbulence has been shown [Walcek and Chang, 1987].

Vertical profiles at Milwaukee, Mayville, and Detroit (Figure 7) show more clearly how the modeled speciesvary with height (from the surface to 2 km above ground level) at individual grid cells. These locations appearin the curtain plots (Figure 6) and are representative of some typical conditions in the region. Milwaukee isrepresentative of a northern urban (high NOx) location with moderate nitrate production, low ammoniaavailability, and a surface gas ratio ~1. Mayville is representative of a rural (low NOx) location with moderatenitrate production, low to moderate ammonia availability, and a surface gas ratio of ~1.5. Detroit isrepresentative of a northern urban location with high nitrate production, high ammonia availability at thesurface, a surface gas ratio of ~2.5, and low ammonia availability aloft. Of these three sites, Mayville has thehighest surface ozone concentration, followed by Milwaukee, and then Detroit. While Detroit and Milwaukeeare similar in that they are both industrial northern cities, Detroit has higher population (3.7 million for theDetroit metro area versus 2.0 for the Milwaukee metro area), higher NOx emissions (by about a factor of 1.8 inthe 2011 NEI), and more ammonia emissions concentrated within a similarly sized metro area. Detroittherefore shows somewhat higher TNO3 production.

Figure 7 depicts vertical profiles of nitrate and N2O5 (left side), ozone, gas ratio, and ammonia (middle panel),and HNO3 formation rates (right panel). Species and processes can be classified as monotonicallydecreasing/increasing with height, or as having peaks at intermediate elevations. Ammonia, aerosol nitrate, andthe reaction rates for the daytime reaction pathways monotonically decrease with height. Total nitrateconcentration is nearly monotonically decreasing, except in cases where layer 1 has slightly lower levels thanlayer 2. Ozonemonotonically increases with height on average. The other variables considered (nitric acid, N2O5,and the nighttime pathway production rate) have a maximum at an intermediate elevation, and aerosol nitratefraction has a minimum at an intermediate elevation. The peak elevation for species that have an intermediateelevation peak is (on average) ~300–400m inMilwaukee andDetroit, and ~100–300m inMayville. Compared tothe daytime, nitric acid and total nitrate concentrations increase at most elevations and all three sites at night.The diurnal variability in aerosol nitrate (day vs. night) is smaller and more variable than total nitrate, withapproximately no change at Milwaukee, and small nocturnal surface increases at Mayville and Milwaukee.

3.6. Visualizing Chemical and Physical Transformations of Ammonia and NOy Species

Diagrams showing quantities of species and rates of transformation between related species can be useful forvisualizing key processes, limiting rates and reactants. We refer to these as flux and reservoir diagrams, where“reservoir” is the concentration of a chemical species or group of species, and “flux” is the rate of chemical orphysical addition or removal to a reservoir. Figure 8 diagrams the system under daytime (Figure 8a) andnighttime (Figure 8b) periods at Milwaukee during modeled episode periods. The corresponding figures forMayville are shown for daytime (Figure 8c) and nighttime (Figure 8d).



Perhaps the most conspicuous feature of Figure 8 is the size of the urban NOx reservoirs (right-hand side ofFigures 8a and 8b) compared to all other reservoirs in the system. At the urban location, NOx is by far thedominant form of NOy with a NOx/NOy ratio of 0.79. This is similar to the fresh emission NOx/NOy ratio of ~0.9[Slowik et al., 2011] which is used as a chemical clock because it decreases with oxidation.

The flux and reservoir diagrams give visual emphasis to the fact that the NOy system during episodeformation consists of a large NOx reservoir that is slowly transformed to nitrate on a regional basis, often (butnot always) passing through the reactive intermediate forms of the NO3 radical, N2O5, and other forms of NOy

such as HONO and PAN. The NOx is relatively long-lived with respect to depositional removal, and much ofthe relevant chemistry occurs aloft and in locations outside of the urban NOx emission hot spots.

Moving through the flux and reservoir diagrams from the primary precursor (NOx) to the species of concern(aerosol nitrate), the flux and reservoir diagrams reinforce what has been shown already from formation rateanalysis (section 3.2), diurnal profiles (section 3.3), and the vertical profiles (3.5). The main fate of NOx duringthe daytime is reaction with OH via that daytime pathway, labeled in these figures by the red flux arrow “rxn b”.There is (not shown) rapid cycling between NOx and other NOy, resolved by CMAQ, but only a small net flux(“rxn a”). It should be noted that while fluxes b and e are for individual reactions, and flux d is the sum of thetwo homogeneous reactions of N2O5 with water vapor, the other fluxes or reactions (a, c, f, and g) are the

Figure 7. Vertical profiles during the episodes buildup periods at (a) Milwaukee, (b) Mayville, and (c) Detroit. Profiles areshown separately averaged over a daytime period (8:00 to 19:00, solid lines) versus a nighttime period (19:00 to 8:00LST, dashed lines).



Figure 8. Flux and reservoir diagrams for episode buildup periods at (a) Milwaukee daytime, (b) Milwaukee nighttime,(c) Mayville daytime, and (d) Mayville nighttime. Reservoir amounts in units ofμmole N/m2. All fluxes are in units ofμmoleN/m2-h. Arrows and reservoirs are sized to show relative amounts. Model process net fluxes (from integrated process rate (IPR)analysis) are shown for fine aerosol and gases in the aerosol process (A), other fluxes in the aerosol process including buildupto coarse mode (A*), the sum or horizontal advection and diffusion (H), the sum of vertical advection and diffusion (V),emissions (E), and dry deposition (DDEP). Reactions have been grouped as follows and are further specified in Table 1: (a) netNO3 radical formation; (b) NO2 +OH!HNO3 (red, daytime pathway); (c) net N2O5 formation; (d) homogenous formation ofHNO3 from N2O5 (blue, homogeneous portion of nighttime pathway); (e) heterogeneous formation of HNO3 from N2O5(green, heterogeneous portion of nighttime pathway); and (g) HNO3 formation from the NO3 radical.



netresult of several reactions, and thus can be positive or negative. Each reaction group is defined in Table 1. Forexample, reaction group a is discussed as net NO3 formation, and it is the sum of two formation reactions andfour destruction reactions.

During the nighttime, the main pathway for formation of TNO3 is represented by the green pathway (“rxn e”),which is the heterogeneous conversion of N2O5. This requires net formation of NO3 and N2O5 by pathways aand c. At night over Milwaukee, these processes are most effective aloft. At Mayville at night, the direction ofreaction a (net NO3 formation) and reaction c (net N2O5 formation) are the same throughout the lower

Figure 8. (continued)



troposphere, resulting in conversion of NOx to NO3, N2O5, and other NOy species. From the surface to 106mat Milwaukee, this process runs in the other direction, with net transformation of N2O5 to NO3, and from NO3

to NOx.

Once N2O5 is oxidized, CMAQ partitions the resulting HNO3 into either NO3! or HNO3, and IPR can be used to

track and visualize this. The flux and reservoir analysis shows that surface aerosol nitrate concentrationincreases are due to (a) downward transport of HNO3 from aloft followed by (b) partitioning to the aerosolphase facilitated by high availability of ammonia at the surface. As modeled in CMAQ, this downwardtransport can largely be attributed to turbulent diffusion as opposed to vertical advection (>89%), consistentwith the stagnant conditions during episodes. Despite the relatively flat or decreasing lapse rate of totalnitrate near the surface (see Figure 7), the net flux of TNO3 between the surface and 100m is alwaysdownward, with the downward HNO3 flux exceeding the upward net upward NO3

!flux. In other words, TNO3

formed aloft and partitioned to the gas phase as HNO3 is fueling accumulation of fine aerosol nitrate at thesurface layer at both sites and during both day and night.

The flux and reservoir diagram also helps us rule out at least one process that (on average) is not important inthe simulations at these sites. Figure 7 shows ammonia availability decreasing with height, and a possibleconsequence of this would be net evaporation of ammonia from aerosols at high elevation in order tomaintain equilibrium. However, Figure 8 shows net conversion of ammonia gas to aerosol ammonium at allelevations and time periods for the two sites examined. In other words, during the episode periods, aerosolsulfate and nitrate production combined with decreasing temperature and increasing relative humidity athigher elevations are, on average, sufficient to always draw ammonia into the aerosol phase at elevationsbelow 1 km.

On the other hand, the flux and reservoir analysis shows that, on average, only a fraction of the total nitrateformed partitions to the aerosol phase. Either due to insufficient ammonia or due to competition for availableammonia with sulfate, some TNO3 that is formed exists as HNO3 rather than aerosol nitrate. This is mostapparent for Mayville at night, where strong conversion of N2O5 to TNO3 aloft (layers 3–4) leads toHNO3 increases.

Nitrogen that is in the model as coarse aerosol nitrate is not included as a reservoir, and the flux of nitrogen tothe coarse mode (calculated in the aerosol module) is shown as the A* flux. At high altitudes, it can be asignificant sink of reactive nitrogen. The average spatial maps of coarse nitrate are included insupplementary material.

The flux and reservoir diagrams support calculation of lifetimes for each reservoir, as the lifetime can becalculated by dividing the size of the reservoir by its net flux. From this method, the dry depositional lifetimesfor NOx are ~20 hours and are the same in both locations. On the other hand, for ammonium nitrate, N2O5,and nitric acid, dry deposition lifetimes are four times shorter in Milwaukee than at the Mayville grid cell. This,in part, explains the vertical profile of nighttime HNO3 production at Mayville (Figure 7b), which does notdecrease at the surface in the same way it did at Milwaukee. Also, nitrate phase is extremely important tolifetime and therefore for the ability of nitrate production to lead to accumulation and high surfaceconcentrations. The dry depositional lifetime of nitric acid in layer 1 is less than 0.25 to 0.35 h depending ontime period and location, while the lifetime of aerosol nitrate is 3–20 h.

Flux rates and reservoir sizes for a grid cell in the Ohio River Valley (not shown) were calculated and comparedto those in Figure 8 for Mayville. The lifetime of N2O5 with respect to heterogeneous processing in the peakproduction layers during Mayville nights was 2–3h, while in the Ohio River Valley the lifetime with respect toaerosol processing was shorter (0.7–1.6 h). This is explained by the fact that γN2O5 values were a factor of 2–3higher in the Ohio River Valley relative to those in Wisconsin. The total flux from heterogeneous processing was3.4μmolem!2 h!1 in Mayville, while in the Ohio River Valley cell it was 5.3μmolem!2 h!1.

3.7. Uncertainty in Gas Phase Rate Coefficients

The rate coefficients for the gas phase reactions producing HNO3 are uncertain. For the nighttime gas phasereactions, R3 and R4, the CMAQ CB05 mechanism used in this work has a temperature independentrate coefficient for R3 (N2O5 +H2O! 2HNO3) of 2.5! 10!22 cm3molec!1 s!1, and a rate coefficient for (R4)(N2O5 + 2H2O! 2HNO3 +H2O) of 1.8! 10!39 cm6molec!2 s!1. These are based on the experimental values



obtained by Wahner et al. [1998]. However, aircraft and ground studies of N2O5 lifetimes at temperatures inthe range of 280–295 K suggest that these values are too high, by up to a factor of ten [Aldener et al., 2006;Brown et al., 2006; Brown et al., 2009; Crowley et al., 2010], and the current the International Union of Pureand Applied Chemistry (IUPAC) recommendation reads “until more laboratory or field data are available tobetter define the rate coefficient we do not recommend incorporation of this reaction in models of theatmosphere but set an upper limit of 1! 10!22 cm3molec!1 s!1 for the bimolecular process only.” [InternationalUnion of Pure and Applied Chemistry (IUPAC), 2010b]. Contribution in CMAQ from (R3) and (R4) was evaluated byIRR and was similar in magnitude to one another. Downward revision of the rates of R3 and R4 to the IUPACrecommended values would reduce the homogenous nighttime contribution by a factor of about 2.5 to 10.

The rate coefficient for the daytime pathway R1 (OH+NO2) is also uncertain. Our work used the termolecularrate parameters in the CB05CLmechanismwhich are ko = 2.0! 10

!30, N=3, k!=2.5! 10!11, and Fc = 0.6. These

match the Jet Propulsion Laboratory (JPL) evaluation 14 recommendation [Sander et al., 2003], hereafterreferred to as JPL03. These were compared to the parameterization published by Mollner et al. [2010], and therecommendation of Henderson et al. [2012], which were based in part on Mollner and coworkers withtemperature sensitivity assigned from inverse modeling. Other comparisons were to IUPAC04 [Atkinson et al.,2004], to the JPL recommendation [Sander et al., 2011] updated starting in Evaluation 15 and unchangedsince then (referred to as JPL11), as well as the updated IUPAC12 recommendation on the NO2+OH reactionreleased in June 2012 [IUPAC, 2010a]. The IUPAC12 values are based on a synthesis of recent experimental data,including the data set of Mollner et al. [2010], performed by Troe [2012]. IUPAC12 recommends N=4.5 whichincreases the sensitivity of the reaction to temperature relative to all other parameterizations.

Evaluated at the pressures and temperatures most relevant for nitric acid formation in this study, the JPL03rate used for these CMAQ runs is within 1 to 5% of the rate coefficients of IUPAC12 and JPL11, and thereforethe daytime formation rates quantified in this work are applicable to those mechanisms as well. IUPAC04are rate coefficients are about 18% higher than those used in this work (i.e., JPL03). The rate coefficientparameterizations proposed by Henderson et al. [2012] and Mollner et al. [2010] are about 16 and 12%lower than those used in this study, respectively. Additional details on the various NO2 +OH rate coefficientscan be found in supplemental material.

4. Conclusions

Wintertime ammonium nitrate in the U.S. Midwest is an important air quality concern, complicated by thefact that multiple pathways exist for the formation of aerosol nitrate. CMAQ-WRF was employed for modelingthis system, and the process analysis tool embedded in CMAQ was utilized to investigate the nitrate system.The homogenous reaction of N2O5 with water vapor and the heterogeneous reaction of N2O5 on hydratedaerosols were tracked as the nighttime pathway. These pathways represented over 95% of the total HNO3

formation in the region. Averaging temporally over the 3month period, and spatially averaging across thehigh-nitrate area, 72% of HNO3 formation can be attributed to the nighttime pathway. Contributions of thehomogeneous reactions of N2O5 to the nighttime pathway were not negligible, representing 28% of thenighttime pathway and 15% of overall HNO3 formation in March.

The lack of reactions of N2O5 with dissolved chloride to form ClNO2 as well as the unaccounted for reductionin γN2O5 on organic aerosols means that the TNO3 production rates calculated in the CMAQ modeling of theLADCO winter nitrate study are upper limits. While both these effects need further study and incorporationinto future model releases because of very large impacts in some seasons and regions, they may be relativelysmall impacts in the upper Midwest during nitrate episodes. The impact of ClNO2, based on the work ofSarwar et al. [2012], is probably less than ~6% in the high aerosol nitrate portions of the domain, and theimpact of the organic suppression of γN2O5 is probably limited by the high RH, low secondary organic aerosol,and high nitrate fraction. Nevertheless, future measurement campaigns on cold weather nitrate shouldinclude measurement of aerosol Cl! and gas phase ClNO2. Model skill for coarse nitrate, including its spatialvariability, temporal patterns, and vertical profile should be assessed through observations.

The NOy system during episodes can be characterized as a large NOx reservoir, resistant to depositionalremoval, that undergoes gradual transformation into nitrate, often (but not always) passing through thereactive intermediate forms of the NO3 radical, N2O5, and other forms of NOy such as HONO and PAN.



Variation in the reactant concentrations causes a high degree of variability on day-to-day and synoptictimescales in the formation rates. Changes in the effective rate coefficients of the various HNO3 formationpathways further contribute to variability.

TNO3 production is mainly a regional phenomenon, with rates greater than 100μmole/m2-day for most ofthe domain. The model places the peak of TNO3 formation during nitrate episodes in the southern GreatLakes and the Ohio River Valley. Urban enhancements in the daytime formation pathway offset to a largeextent urban deficiencies in the nighttime pathway. Decreased nighttime TNO3 formation in some urbanlocations can be attributed to low O3 which suppresses NO3 radical formation.

High aerosol nitrate (Figure 6b) at the surface during the episodes appears throughout the Midwest overland, while over the Great Lakes higher temperatures and lower ammonia availability favor partitioning toHNO3. Due to short depositional lifetimes for HNO3 and some of its precursors (N2O5, NO3 radical), and due toammonia gradients which favor particulate nitrate at the surface and HNO3 aloft, nitric acid (Figure 6c) isdepleted in the surface layer and has peak concentrations from 200 to 500m above the ground.

The aerosol nitrate fraction (Figure 6d) has a strong vertical gradient, with aerosol phase favored at thesurface. In northern urban areas, local ammonia emissions likely contribute to local enhancement of theaerosol nitrate fraction. Diurnal patterns of the aerosol nitrate fraction are relatively flat, without substantialdifference in the daytime and nighttime fractions during episodes. This is due to high nitric acid productionvia the nighttime pathway and from temperature and relative humidities sufficient to support partitioning tothe aerosol phase at all times of the day. Surface aerosol nitrate concentration increases are due to (a)transport of nitric acid from aloft followed by (b) partitioning to the aerosol phase facilitated by highavailability of ammonia at the surface. Inspection of flux and reservoir diagrams for key types of NOy duringepisodes at Milwaukee and Mayville allows visualization of the interplay of chemical and physical processescontrolling TNO3 formation, partitioning, and accumulation. Nitric acid formed aloft fuels accumulation ofaerosol nitrate at the surface layer at both sites. This is true for both daytime and nighttime episode periods.

Collectively, these findings support refinement of the conceptual model of wintertime fine particle events:regional nitrate is formed by nighttime heterogeneous reactions aloft, followed by downward transport byturbulent diffusion and associated partitioning to the aerosol phase. Rates of all three pathways for NOx to TNO3

conversion are uncertain, and none of them are negligible in the CMAQ v.4.7.1 model configuration of thisstudy. Although rates are uncertain, these qualitative results should be relatively robust to uncertainties in themodeled processes, emission inventories, modeled PBL dynamics, and aerosol thermodynamics. Whether ornot the homogeneous nighttime reaction is truly a significant contributor to NOx removal remains uncertain, asa downward revision of a factor of 10 would make it a negligible contributor on average.

This study demonstrates that while raw IPR and IRR results can be difficult to interpret, post processing of IPRand IRR can trace atmospheric transformation pathways that encompass multiple chemical andmicrophysical processes. Using this approach we examined transformation through flux and reservoirquantities, and horizontal, vertical, and temporal averaging to interpret process analysis results. Asrecommended in Chang et al. [2011], future field campaigns that aim to further validate wintertime nitratemodels or elucidate wintertime or cold pool nitrate formation need to measure a wide variety of NOy species,include chemical and meteorological vertical profiling, and thus enable quantification of as many of thefluxes and reservoirs (Figure 8) as possible. Vertical profiling of NOy reservoir species in the boundary layer isnow being incorporated into some field campaigns using airborne in situ sampling and DOAS techniques[e.g., Brown and Stutz, 2012; Young et al., 2012] and tall towers [Brown et al., 2013].

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