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Chemical mechanisms describe the atmospheric transformations of organic and inorganic species and connect air emissions to secondary species such as ozone, fine particles, and hazardous air pollutants (HAPs) like formaldehyde. Recent advances in our understanding of several chemical systems and shifts in the drivers of atmospheric chemistry warrant updates to mechanisms used in chemical transport models such as the Community Multiscale Air Quality (CMAQ) modeling system. This work builds on the Regional Atmospheric Chemistry Mechanism version 2 (RACM2) and develops the Community Regional Atmospheric Chemistry Multiphase Mechanism (CRACMM) version 1.0, which demonstrates a fully coupled representation of chemistry leading to ozone and secondary organic aerosol (SOA) with consideration of HAPs. CRACMMv1.0 includes 178 gas-phase species, 51 particulate species, and 508 reactions spanning gas-phase and heterogeneous pathways. To support estimation of health risks associated with HAPs, nine species in CRACMM cover 50 % of the total cancer and 60 % of the total non-cancer emission-weighted toxicity estimated for primary HAPs from anthropogenic and biomass burning sources in the US, with the coverage of toxicity higher (> 80 %) when secondary formaldehyde and acrolein are considered. In addition, new mechanism species were added based on the importance of their emissions for the ozone, organic aerosol, or atmospheric burden of total reactive organic carbon (ROC): sesquiterpenes, furans, propylene glycol, alkane-like low- to intermediate-volatility organic compounds (9 species), low- to intermediate-volatility oxygenated species (16 species), intermediate-volatility aromatic hydrocarbons (2 species), and slowly reacting organic carbon. Intermediate- and lower-volatility organic compounds were estimated to increase the coverage of anthropogenic and biomass burning ROC emissions by 40 % compared to current operational mechanisms. Autoxidation, a gas-phase reaction particularly effective in producing SOA, was added for C10 and larger alkanes, aromatic hydrocarbons, sesquiterpenes, and monoterpene systems including second-generation aldehydes. Integrating the radical and SOA chemistry put additional constraints on both systems and enabled the implementation of previously unconsidered SOA pathways from phenolic and furanone compounds, which were predicted to account for ∼ 30 % of total aromatic hydrocarbon SOA under typical atmospheric conditions. CRACMM organic aerosol species were found to span the atmospherically relevant range of species carbon number, number of oxygens per carbon, and oxidation state with a slight high bias in the number of hydrogens per carbon. In total, 11 new emitted species were implemented as precursors to SOA compared to current CMAQv5.3.3 representations, resulting in a bottom-up prediction of SOA, which is required for accurate source attribution and the design of control strategies. CRACMMv1.0 is available in CMAQv5.4.

The fourth phase of the Air Quality Model Evaluation International Initiative (AQMEII4) is conducting a diagnostic intercomparison and evaluation of deposition simulated by regional-scale air quality models over North America and Europe. In this study, we analyze annual AQMEII4 simulations performed with the Community Multiscale Air Quality Model (CMAQ) version 5.3.1 over North America. These simulations were configured with both the M3Dry and Surface Tiled Aerosol and Gas Exchange (STAGE) dry deposition schemes available in CMAQ. A comparison of observed and modeled concentrations and wet deposition fluxes shows that the AQMEII4 CMAQ simulations perform similarly to other contemporary regional-scale modeling studies. During summer, M3Dry has higher ozone (O3) deposition velocities (Vd) and lower mixing ratios than STAGE for much of the eastern U.S. while the reverse is the case over eastern Canada and along the West Coast. In contrast, during winter STAGE has higher O3 Vd and lower mixing ratios than M3Dry over most of the southern half of the modeling domain while the reverse is the case for much of the northern U.S. and southern Canada. Analysis of the diagnostic variables defined for the AQMEII4 project, i.e. grid-scale and land-use (LU) specific effective conductances and deposition fluxes for the major dry deposition pathways, reveals generally higher summertime stomatal and wintertime cuticular grid-scale effective conductances for M3Dry and generally higher soil grid-scale effective conductances (for both vegetated and bare soil) for STAGE in both summer and winter. On a domain-wide basis, the stomatal grid-scale effective conductances account for about half of the total O3 Vd during daytime hours in summer for both schemes. Employing LU-specific diagnostics, results show that daytime Vd varies by a factor of 2 between LU categories. Furthermore, M3Dry vs. STAGE differences are most pronounced for the stomatal and vegetated soil pathway for the forest LU categories, with M3Dry estimating larger effective conductances for the stomatal pathway and STAGE estimating larger effective conductances for the vegetated soil pathway for these LU categories. Annual domain total O3 deposition fluxes differ only slightly between M3Dry (74.4 Tg/year) and STAGE (76.2 Tg/yr), but pathway-specific fluxes to individual LU types can vary more substantially on both annual and seasonal scales which would affect estimates of O3 damages to sensitive vegetation. A comparison of two simulations differing only in their LU classification scheme shows that the differences in LU cause seasonal mean O3 mixing ratio differences on the order of 1 ppb across large portions of the domain, with the differences generally largest during summer and in areas characterized by the largest differences in the fractional coverages of the forest, planted/cultivated, and grassland LU categories. These differences are generally smaller than the M3Dry vs. STAGE differences outside the summer season but have a similar magnitude during summer. Results indicate that the deposition impacts of LU differences are caused both by differences in the fractional coverages and spatial distributions of different LU categories as well as the characterization of these categories through variables like surface roughness and vegetation fraction in look-up tables used in the land-surface model and deposition schemes. Overall, the analyses and results presented in this study illustrate how the diagnostic grid-scale and LU-specific dry deposition variables adopted for AQMEII4 can provide insights into similarities and differences between the CMAQ M3Dry and STAGE dry deposition schemes that affect simulated pollutant budgets and ecosystem impacts from atmospheric pollution.

Wildfire activity is increasing in the continental U.S. and can be linked to climate change effects and nutrient emissions, with potential effects on downwind ecosystems.

Chemical mechanisms describe the atmospheric transformations of organic and inorganic species and connect air emissions to secondary species such as ozone, fine particles, and hazardous air pollutants (HAPs) like formaldehyde. Recent advances in our understanding of several chemical systems and shifts in the drivers of atmospheric chemistry warrant updates to mechanisms used in chemical transport models such as the Community Multiscale Air Quality (CMAQ) modeling system. This work builds on the Regional Atmospheric Chemistry Mechanism version 2 (RACM2) and develops the Community Regional Atmospheric Chemistry Multiphase Mechanism (CRACMM) version 1.0, which fully couples the chemistry leading to ozone and secondary organic aerosol (SOA) with consideration of HAPs. CRACMM v1.0 includes 178 gas-phase species, 51 particulate species, and 508 reactions spanning gas-phase and heterogeneous pathways. To support estimation of health risks associated with HAPs, nine species in CRACMM cover 50 % of the total cancer and 60 % of the total noncancer health risk estimated for primary HAPs from anthropogenic and biomass burning sources in the U.S., with the coverage of risk higher (>80 %) when secondary formaldehyde and acrolein are considered. In addition, new mechanism species were added based on the importance of their emissions for ozone, organic aerosol, or atmospheric burden of total reactive organic carbon (ROC): sesquiterpenes, furans, propylene glycol, alkane-like low to intermediate volatility organic compounds (9 species), low to intermediate volatility oxygenated species (16 species), intermediate volatility aromatic hydrocarbons (2 species), and slowly reacting organic carbon. Intermediate and lower volatility organic compounds were estimated to increase the coverage of anthropogenic and biomass burning ROC emissions by 40 % compared to current operational mechanisms. Autoxidation, a gas-phase reaction particularly effective in producing SOA, was added for C10 and larger alkanes, aromatic hydrocarbons, sesquiterpenes, and monoterpene systems including second generation aldehydes. Integrating the radical and SOA chemistry put additional constraints on both systems and enabled the implementation of previously unconsidered SOA pathways from phenolic and furanone compounds, which were predicted to account for ~30 % of total aromatic hydrocarbon SOA under typical atmospheric conditions. CRACMM organic aerosol species were found to span the atmospherically relevant range of carbon number, number of oxygens per carbon, and oxidation state with a slight high bias in number of hydrogens per carbon. In total, eleven new emitted species were implemented as precursors to SOA compared to current CMAQv5.3.3 representations resulting in a bottom-up prediction of SOA, which is required for accurate source attribution and design of control strategies. CRACMMv1.0 will be available in CMAQv5.4.

As the climate warms, wildfire activity is increasing, posing a risk to human health. Studies have reported on particulate matter (PM) in wildfire smoke, yet the chemicals associated with PM have received considerably less attention. Here, we analyzed 13 years (2006–2018) of PM(2.5) chemical composition data from monitors in California on smoke-impacted days. Select chemicals (e.g., aluminum and sulfate) were statistically elevated on smoke-impacted days in over half of the years studied. Other chemicals, mostly trace metals harmful to human health (e.g., copper and lead), were elevated during particular fires only. For instance, in 2018, lead was more than 40 times higher on smoke days on average at the Point Reyes monitoring station due mostly to the Camp Fire, burning approximately 200 kilometers away. There was an association between these metals and the combustion of anthropogenic material (e.g., the burning of houses and vehicles). Although still currently rare, these infrastructure fires are likely becoming more common, and can mobilize trace metals in smoke far downwind, at levels generally unseen except in the most polluted areas of the country. We hope a better understanding of the chemicals in wildfire smoke will assist in the communication and reduction of public health risks.

Atmospheric deposition of nitrogen (N) and sulfur (S) compounds from human activity has greatly declined in the United States (US) over the past several decades in response to emission controls set by the Clean Air Act. While many observational studies have investigated spatial and temporal trends of atmospheric deposition, modeling assessments can provide useful information over areas with sparse measurements, although they usually have larger horizontal resolutions and are limited by input data availability. In this analysis, we evaluate wet, dry, and total N and S deposition from multiyear simulations within the contiguous US (CONUS). Community Multiscale Air Quality (CMAQ) model estimates from the EPA's (Environmental Protection Agency) Air QUAlity TimE Series (EQUATES) project contain important model updates to atmospheric deposition algorithms compared to previous model data, including the new Surface Tiled Aerosol and Gaseous Exchange (STAGE) bidirectional deposition model which contains land-use-specific resistance parameterization and land-use-specific deposition estimates needed to estimate the differential impacts of N deposition to different land use types. First, we evaluate model estimates of wet deposition and ambient concentrations, finding underestimates of SO4, NO3, and NH4 wet deposition compared to National Atmospheric Deposition Program observations and underestimates of NH4 and SO4 and overestimates of SO2 and TNO3 (HNO3+NO3) compared to the Clean Air Status and Trends Network (CASTNET) ambient concentrations. Second, a measurement–model fusion approach employing a precipitation and bias correction to wet-deposition estimates is found to reduce model bias and improve correlations compared to the unadjusted model values. Model agreement of wet deposition is poor over parts of the West and Northern Rockies, due to errors in precipitation estimates caused by complex terrain and uncertainty in emissions at the relatively coarse 12 km grid resolution used in this study. Next, we assess modeled N and S deposition trends across climatologically consistent regions in the CONUS. Total deposition of N and S in the eastern US is larger than the western US with a steeper decreasing trend from 2002–2017; i.e., total N declined at a rate of approximately −0.30 kg N ha−1 yr−1 in the Northeast and Southeast and by −0.02 kg N ha−1 yr−1 in the Northwest and Southwest. Widespread increases in reduced N deposition across the Upper Midwest, Northern Rockies, and West indicate evolving atmospheric composition due to increased precipitation amounts over some areas, growing agricultural emissions, and regional NOx/SOx emission reductions shifting gas–aerosol partitioning; these increases in reduced N deposition are generally masked by the larger decreasing oxidized N trend. We find larger average declining trends of total N and S deposition between 2002–2009 than 2010–2017, suggesting a slowdown of the rate of decline likely in response to smaller emission reductions. Finally, we document changes in the modeled total N and S deposition budgets. The average annual total N deposition budget over the CONUS decreases from 7.8 in 2002 to 6.3 kg N ha−1 yr−1 in 2017 due to declines in oxidized N deposition from NOx emission controls. Across the CONUS during the 2002–2017 time period, the average contribution of dry deposition to the total N deposition budget drops from 60 % to 52 %, whereas wet deposition dominates the S budget rising from 45 % to 68 %. Our analysis extends upon the literature documenting the growing contribution of reduced N to the total deposition budget, particularly in the Upper Midwest and Northern Rockies, and documents a slowdown of the declining oxidized N deposition trend, which may have consequences on vegetation diversity and productivity.

Modeled source attribution information from the Community Multiscale Air Quality model was coupled with ambient data from the 2011 Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality Baltimore field study. We assess source contributions and evaluate the utility of using aircraft measured CO and NO

This study assesses the impact of the lightning nitric oxide (LNO) production schemes in the Community Multiscale Air Quality (CMAQ) model on ground-level air quality as well as aloft atmospheric chemistry through detailed evaluation of model predictions of nitrogen oxides (NOx) and ozone (O3) with corresponding observations for the US. For ground-level evaluations, hourly O3 and NOx values from the U.S. EPA Air Quality System (AQS) monitoring network are used to assess the impact of different LNO schemes on model prediction of these species in time and space. Vertical evaluations are performed using ozonesonde and P-3B aircraft measurements during the Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) campaign conducted in the Baltimore–Washington region during July 2011. The impact on wet deposition of nitrate is assessed using measurements from the National Atmospheric Deposition Program's National Trends Network (NADP NTN). Compared with the Base model (without LNO), the impact of LNO on surface O3 varies from region to region depending on the Base model conditions. Overall statistics suggest that for regions where surface O3 mixing ratios are already overestimated, the incorporation of additional NO from lightning generally increased model overestimation of mean daily maximum 8 h (DM8HR) O3 by 1–2 ppb. In regions where surface O3 is underestimated by the Base model, LNO can significantly reduce the underestimation and bring model predictions close to observations. Analysis of vertical profiles reveals that LNO can significantly improve the vertical structure of modeled O3 distributions by reducing underestimation aloft and to a lesser degree decreasing overestimation near the surface. Since the Base model underestimates the wet deposition of nitrate in most regions across the modeling domain with the exception of the Pacific Coast, the inclusion of LNO leads to reduction in biases and errors and an increase in correlation coefficients at almost all the NADP NTN sites. Among the three LNO schemes described in Kang et al. (2019), the hNLDN scheme, which is implemented using hourly observed lightning flash data from National Lightning Detection Network (NLDN), performs best for comparisons with ground-level values, vertical profiles, and wet deposition of nitrate; the mNLDN scheme (the monthly NLDN-based scheme) performed slightly better. However, when observed lightning flash data are not available, the linear regression-based parameterization scheme, pNLDN, provides an improved estimate for nitrate wet deposition compared to the base simulation that does not include LNO.

This work describes the lightning nitric oxide (LNO) production schemes in the Community Multiscale Air Quality (CMAQ) model. We first document the existing LNO production scheme and vertical distribution algorithm. We then describe updates that were made to the scheme originally based on monthly National Lightning Detection Network (mNLDN) observations. The updated scheme uses hourly NLDN (hNLDN) observations. These NLDN-based schemes are good for retrospective model applications when historical lightning data are available. For applications when observed data are not available (i.e., air quality forecasts and climate studies that assume similar climate conditions), we have developed a scheme that is based on linear and log-linear parameters derived from regression of multiyear historical NLDN (pNLDN) observations and meteorological model simulations. Preliminary assessment for total column LNO production reveals that the mNLDN scheme overestimates LNO by over 40 % during summer months compared with the updated hNLDN scheme that reflects the observed lightning activity more faithfully in time and space. The pNLDN performance varies with year, but it generally produced LNO columns that are comparable to hNLDN and mNLDN, and in most cases it outperformed mNLDN. Thus, when no observed lightning data are available, pNLDN can provide reasonable estimates of LNO emissions over time and space for this important natural NO source that influences air quality regulations.

Air quality models provide spatial fields of wet deposition (WD) and dry deposition that explicitly account for the transport and transformation of emissions from thousands of sources. However, many sources of uncertainty in the air quality model including errors in emissions and meteorological inputs (particularly precipitation) and incomplete descriptions of the chemical and physical processes governing deposition can lead to bias and error in the simulation of WD. We present an approach to bias correct Community Multiscale Air Quality model output over the contiguous United States using observation‐based gridded precipitation data generated by the Parameter‐elevation Regressions on Independent Slopes Model and WD observations at the National Atmospheric Deposition Program National Trends Network sites. A cross‐validation analysis shows that the adjusted annual accumulated WD for NO3 −, NH4 +, and SO4 2− from 2002 to 2012 has less bias and higher correlation with observed values than the base model output without adjustment. Temporal trends in observed WD are captured well by the adjusted model simulations across the entire contiguous United States. Consistent with previous trend analyses, WD NO3 − and SO4 2− are shown to decrease during this period in the eastern half of the United States, particularly in the Northeast, while remaining nearly constant in the West. Trends in WD of NH4 + are more spatially and temporally heterogeneous, with some positive trends in the Great Plains and Central Valley of CA and slightly negative trends in the south., Key Points Modeled estimates of the wet deposition across the United States are improved by fusion with gridded precipitation data and observed wet depositionThe fused wet deposition estimates evaluate well against observed values and maintain the spatial heterogeneity in the original model outputTemporal trends in observed wet deposition of nitrate, ammonium, and sulfate from 2002 to 2012 are well captured by the adjusted model output

Daily maximum 8-hour average (MDA8) ozone (O(3)) concentrations are well-known to be influenced by local meteorological conditions, which vary across both daily and seasonal temporal scales. Previous studies have adjusted long-term trends in O(3) concentrations for meteorological effects using various statistical and mathematical methods in order to get a better estimate of the long-term changes in O(3) concentrations due to changes in precursor emissions such as nitrogen oxides (NO(X)) and volatile organic compounds (VOCs). In this work, the authors present improvements to the current method used by the United States Environmental Protection Agency (US EPA) to adjust O(3) trends for meteorological influences by making refinements to the input data sources and by allowing the underlying statistical model to vary locally using a variable selection procedure. The current method is also expanded by using a quantile regression model to adjust trends in the 90(th) and 98(th) percentiles of the distribution of MDA8 O(3) concentrations, allowing for a better understanding of the effects of local meteorology on peak O(3) levels in addition to seasonal average concentrations. The revised method is used to adjust trends in the May to September mean, 90(th) percentile, and 98(th) percentile MDA8 O(3) concentrations at over 700 monitoring sites in the U.S. for years 2000 to 2016. The utilization of variable selection and quantile regression allow for a more in-depth understanding of how weather conditions affect O(3) levels in the U.S. This represents a fundamental advancement in our ability to understand how interannual variability in weather conditions in the U.S. may impact attainment of the O(3) National Ambient Air Quality Standards (NAAQS).

Wildland fire emissions are routinely estimated in the US Environmental Protection Agency’s National Emissions Inventory, specifically for fine particulate matter (PM2.5) and precursors to ozone (O3); however, there is a large amount of uncertainty in this sector. We employ a brute-force zero-out sensitivity method to estimate the impact of wildland fire emissions on air quality across the contiguous US using the Community Multiscale Air Quality (CMAQ) modelling system. These simulations are designed to assess the importance of wildland fire emissions on CMAQ model performance and are not intended for regulatory assessments. CMAQ ver. 5.0.1 estimated that fires contributed 11% to the mean PM2.5 and less than 1% to the mean O3 concentrations during 2008–2012. Adding fires to CMAQ increases the number of ‘grid-cell days’ with PM2.5 above 35 µg m−3 by a factor of 4 and the number of grid-cell days with maximum daily 8-h average O3 above 70 ppb by 14%. Although CMAQ simulations of specific fires have improved with the latest model version (e.g. for the 2008 California wildfire episode, the correlation r = 0.82 with CMAQ ver. 5.0.1 v. r = 0.68 for CMAQ ver. 4.7.1), the model still exhibits a low bias at higher observed concentrations and a high bias at lower observed concentrations. Given the large impact of wildland fire emissions on simulated concentrations of elevated PM2.5 and O3, improvements are recommended on how these emissions are characterised and distributed vertically in the model.

Atmospheric nitrogen oxide and nitrogen dioxide (NO + NO2, together termed as NO X ) estimates from annual photochemical simulations for years 2002-2016 are compared to surface network measurements of NO X and total gas-phase-oxidized reactive nitrogen (NO Y ) to evaluate the Community Multiscale Air Quality (CMAQ) modeling system performance by U.S. region, season, and time of day. In addition, aircraft measurements from 2011 Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality are used to evaluate how emissions, chemical mechanism, and measurement uncertainty each contribute to the overall model performance. We show distinct seasonal and time-of-day patterns in NO X performance. Summertime NO X is overpredicted with bimodal peaks in bias during early morning and evening hours and persisting overnight. The summertime morning NO X bias dropped from between 28% and 57% for earlier years (2002-2012) to between -2% and 7% for later years (2013-2016). Summer daytime NO X tends to be unbiased or underpredicted. In winter, the evening NO X overpredictions remain, but NO X is unbiased or underpredicted overnight, in the morning, and during the day. NO X overpredictions are most pronounced in the Midwestern and Southern United States with Western regions having more of a tendency toward model underpredictions of NO X . Modeled NO X performance has improved substantially over time, reflecting updates to the emission inputs and the CMAQ air quality model. Model performance improvements are largest for years simulated with CMAQv5.1 or later and for emission inventory years 2014 and later, coinciding with reduced onroad NO X emissions from vehicles with newer emission control technologies and improved treatment of chemistry, deposition, and vertical mixing in CMAQ. Our findings suggest that emissions temporalization of specific mobile source sectors have a small impact on model performance, while chemistry updates improve predictions of NO Y but do not improve summertime NO X bias in the Baltimore/DC area. Sensitivity runs performed for different locations across the country suggest that the improvement in summer NO X performance can be attributed to updates in vertical mixing incorporated in CMAQv5.1.

An estimated 4.2 million premature deaths globally are influenced by ambient air pollution, mainly from heart disease, stroke, chronic obstructive pulmonary disease and lung cancer, besides attribu...

This study uses Las Vegas near-road measurements of carbon monoxide (CO) and nitrogen oxides (NOx) to test the consistency of onroad emission constraint methodologies. We derive commonly used CO to NOx ratios (∆CO:∆NOx) from cross-road gradients and from linear regression using ordinary least squares (OLS) regression and orthogonal regression. The CO to NOx ratios are used to infer NOx emission adjustments for a priori emissions estimates from EPA&rsquo, s MOtor Vehicle Emissions Simulator (MOVES) model assuming unbiased CO. The assumption of unbiased CO emissions may not be appropriate in many circumstances but was implemented in this analysis to illustrate the range of NOx scaling factors that can be inferred based on choice of methods and monitor distance alone. For the nearest road estimates (25 m), the cross-road gradient and ordinary least squares (OLS) agree with each other and are not statistically different from the MOVES-based emission estimate while ∆CO:∆NOx from orthogonal regression is significantly higher than the emitted ratio from MOVES. Using further downwind measurements (i.e., 115 m and 300 m) increases OLS and orthogonal regression estimates of ∆CO:∆NOx but not cross-road gradient ∆CO:∆NOx. The inferred NOx emissions depend on the observation-based method, as well as the distance of the measurements from the roadway and can suggest either that MOVES NOx emissions are unbiased or that they should be adjusted downward by between 10% and 47%. The sensitivity of observation-based ∆CO:∆NOx estimates to the selected monitor location and to the calculation method characterize the inherent uncertainty of these methods that cannot be derived from traditional standard-error based uncertainty metrics.

10 pags., 8 figs., 1 tab., We implement oceanic dimethylsulfide (DMS) emissions and its atmospheric chemical reactions into the Community Multiscale Air Quality (CMAQv53) model and perform annual simulations without and with DMS chemistry to quantify its impact on tropospheric composition and air quality over the Northern Hemisphere. DMS chemistry enhances both sulfur dioxide (SO) and sulfate (SO) over seawater and coastal areas. It enhances annual mean surface SO concentration by +46 pptv and SOby +0.33 μg/m and decreases aerosol nitrate concentration by −0.07 μg/m over seawater compared to the simulation without DMS chemistry. The changes decrease with altitude and are limited to the lower atmosphere. Impacts of DMS chemistry on SO are largest in the summer and lowest in the fall due to the seasonality of DMS emissions, atmospheric photochemistry and resultant oxidant levels. Hydroxyl and nitrate radical-initiated pathways oxidize 75% of the DMS while halogen-initiated pathways oxidize 25%. DMS chemistry leads to more acidic particles over seawater by decreasing aerosol pH. Increased SOfrom DMS enhances atmospheric extinction while lower aerosol nitrate reduces the extinction so that the net effect of DMS chemistry on visibility tends to remain unchanged over most of the seawater., Junri Zhao and Yan Zhang are thankful to the foundation of the National Key Research and Development Program of China (2016YFA060130X)

The Community Multiscale Air Quality (CMAQ) model is a comprehensive multipollutant air quality modeling system developed and maintained by the US Environmental Protection Agency's (EPA) Office of Research and Development (ORD). Recently, version 5.1 of the CMAQ model (v5.1) was released to the public, incorporating a large number of science updates and extended capabilities over the previous release version of the model (v5.0.2). These updates include the following: improvements in the meteorological calculations in both CMAQ and the Weather Research and Forecast (WRF) model used to provide meteorological fields to CMAQ, updates to the gas and aerosol chemistry, revisions to the calculations of clouds and photolysis, and improvements to the dry and wet deposition in the model. Sensitivity simulations isolating several of the major updates to the modeling system show that changes to the meteorological calculations result in enhanced afternoon and early evening mixing in the model, periods when the model historically underestimates mixing. This enhanced mixing results in higher ozone (O3) mixing ratios on average due to reduced NO titration, and lower fine particulate matter (PM2. 5) concentrations due to greater dilution of primary pollutants (e.g., elemental and organic carbon). Updates to the clouds and photolysis calculations greatly improve consistency between the WRF and CMAQ models and result in generally higher O3 mixing ratios, primarily due to reduced cloudiness and attenuation of photolysis in the model. Updates to the aerosol chemistry result in higher secondary organic aerosol (SOA) concentrations in the summer, thereby reducing summertime PM2. 5 bias (PM2. 5 is typically underestimated by CMAQ in the summer), while updates to the gas chemistry result in slightly higher O3 and PM2. 5 on average in January and July. Overall, the seasonal variation in simulated PM2. 5 generally improves in CMAQv5.1 (when considering all model updates), as simulated PM2. 5 concentrations decrease in the winter (when PM2. 5 is generally overestimated by CMAQ) and increase in the summer (when PM2. 5 is generally underestimated by CMAQ). Ozone mixing ratios are higher on average with v5.1 vs. v5.0.2, resulting in higher O3 mean bias, as O3 tends to be overestimated by CMAQ throughout most of the year (especially at locations where the observed O3 is low); however, O3 correlation is largely improved with v5.1. Sensitivity simulations for several hypothetical emission reduction scenarios show that v5.1 tends to be slightly more responsive to reductions in NOx (NO + NO2), VOC and SOx (SO2 + SO4) emissions than v5.0.2, representing an improvement as previous studies have shown CMAQ to underestimate the observed reduction in O3 due to large, widespread reductions in observed emissions.

The impact of climate change on human and environmental health is of critical concern. Population exposures to air pollutants both indoors and outdoors are influenced by a wide range of air quality, meteorological, behavioral, and housing-related factors, many of which are also impacted by climate change. An integrated methodology for modeling changes in human exposures to tropospheric ozone (O(3)) due to potential future changes in climate and demographics was implemented by linking existing modeling tools for climate, weather, air quality, population distribution, and human exposure. Human exposure results from the Air Pollutants Exposure Model (APEX) for 12 U.S. cities show differences in daily maximum 8-hour (DM8H) exposure patterns and levels by sex, age, and city for all scenarios. When climate is held constant and population demographics are varied, minimal difference in O(3) exposures was predicted even with the most extreme demographic change scenario. In contrast, when population is held constant, we see evidence of substantial changes in O(3) exposure for the most extreme change in climate. Similarly, we see increases in the percentage of the population in each city with at least one O(3) exposure exceedance above 60 and 70 ppb thresholds for future changes in climate. For these climate and population scenarios, the impact of projected changes in climate and air quality on human exposure to O(3) are much larger than the impacts of changing demographics. These results indicate the potential for future changes in O(3) exposure as a result of changes in climate that could impact human health.

Nitrogen oxides (NOX: NO + NO2) play a critical role in controlling atmospheric chemistry, especially for the tropospheric ozone (O3) formation and distribution.

10 pags., 11 figs., 1 tab., Bromine and iodine chemistry has been updated in the Community Multiscale Air Quality (CMAQ) model to better capture the influence of natural emissions from the oceans on ozone concentrations. Annual simulations were performed using the hemispheric CMAQ model without and with bromine and iodine chemistry. Model results over the Northern Hemisphere show that including bromine and iodine chemistry in CMAQ not only reduces ozone concentrations within the marine boundary layer but also aloft and inland. Bromine and iodine chemistry reduces annual mean surface ozone over seawater by 25%, with lesser ozone reductions over land. The bromine and iodine chemistry decreases ozone concentration without changing the diurnal profile and is active throughout the year. However, it does not have a strong seasonal influence on ozone over the Northern Hemisphere. Model performance of CMAQ is improved by the bromine and iodine chemistry when compared to observations, especially at coastal sites and over seawater. Relative to bromine, iodine chemistry is approximately four times more effective in reducing ozone over seawater over the Northern Hemisphere (on an annual basis). Model results suggest that the chemistry modulates intercontinental transport and lowers the background ozone imported to the United States.

This study assesses the impact of the lightning NOX (LNOX) production schemes in the CMAQ model (Kang et al., 2019) on ground-level air quality as well as aloft atmospheric chemistry through detailed evaluation of model predictions of nitrogen oxides (NOx) and ozone (O3) with corresponding observations for the U.S. For ground-level evaluations, hourly O3 and NOx from the US EPA's AQS monitoring network are used to assess the impact of different LNOx schemes on model prediction of these species in time and space. Vertical evaluations are performed using ozonesonde and P-3B aircraft measurements during the DISCOVER-AQ campaign conducted in the Baltimore/Washington region during July 2011. The impact on wet deposition of nitrate is assessed using measurements from the National Atmospheric Deposition Program's National Trends Network (NADP/NTN). Compared with the base model (without LNOx), the impact of LNOx on surface O3 varies from region to region depending on the base model conditions. Overall statistics suggest that for regions where surface O3 mixing ratios are already overestimated, the incorporation of additional NOx from lightning generally increased model overestimation of mean daily maximum 8-hr (DM8HR) O3 by 1–2 ppb. In regions where surface O3 is underestimated by the base model, LNOx can significantly reduce the underestimation and bring model predictions close to observations. Analysis of vertical profiles reveals that LNOx can significantly improve the vertical structure of modeled O3 distributions by reducing underestimation aloft, and to a lesser degree decreasing overestimation near the surface. Since the base model underestimates the wet deposition of nitrate in most regions across the modeling domain except the Pacific Coast, the inclusion of LNOx leads to reduction in biases and errors and an increase in correlation coefficients at almost all the NADP/NTN sites. Among the three LNOx schemes described in Kang et al. (2019), the hNLDN scheme, which is implemented using hourly observed lightning flash data from National Lightning Detection Network (NLDN), performs best for the ground-level, vertical profiles, and wet deposition comparisons except that for the accumulated wet deposition of nitrate, the mNLDN scheme (the monthly NLDN-based scheme) performed slightly better. However, when observed lightning flash data are not available, the linear regression-based parameterization scheme, pNLDN, provides an improved estimate for LNOx compared to the base simulation that does not include LNOx.

Surface layer resistance plays an important role in determining ozone deposition velocity over sea-water and can be influenced by chemical interactions at the air-water interface. Here, we examine the effect of chemical interactions of iodide, dimethylsulfide, dissolved organic carbon, and bromide in seawater on ozone deposition. We perform a series of simulations using the hemispheric Community Multiscale Air Quality model for summer months in the Northern Hemisphere. Our results suggest that each chemical interaction enhances the ozone deposition velocity and decreases the atmospheric ozone mixing ratio over seawater. Iodide enhances the median deposition velocity over seawater by 0.023 cm s−1, dissolved organic carbon by 0.021 cm s−1, dimethylsulfide by 0.002 cm s−1, and bromide by ∼0.0006 cm s−1. Consequently, iodide decreases the median atmospheric ozone mixing ratio over seawater by 0.7 ppb, dissolved organic carbon by 0.8 ppb, dimethylsulfide by 0.1 ppb, and bromide by 0.02 ppb. In a separate model simulation, we account for the effect of dissolved salts in seawater on the Henry’s law constant for ozone and find that it reduces the median deposition velocity by 0.007 cm s−1 and increases surface ozone mixing ratio by 0.2 ppb. The combined effect of these processes increases the median ozone deposition velocity over seawater by 0.040 cm s−1, lowers the atmospheric ozone mixing ratio by 5%, and slightly improves model performance relative to observations.

© 2015 American Chemical Society. Fate of ozone in marine environments has been receiving increased attention due to the tightening of ambient air quality standards. The role of deposition and halogen chemistry is examined through incorporation of an enhanced ozone deposition algorithm and inclusion of halogen chemistry in a comprehensive atmospheric modeling system. The enhanced ozone deposition treatment accounts for the interaction of iodide in seawater with ozone and increases deposition velocities by 1 order of magnitude. Halogen chemistry includes detailed chemical reactions of organic and inorganic bromine and iodine species. Two different simulations are completed with the halogen chemistry: without and with photochemical reactions of higher iodine oxides. Enhanced deposition reduces mean summer-time surface ozone by ∼3% over marine regions in the Northern Hemisphere. Halogen chemistry without the photochemical reactions of higher iodine oxides reduces surface ozone by ∼15% whereas simulations with the photochemical reactions of higher iodine oxides indicate ozone reductions of ∼48%. The model without these processes overpredicts ozone compared to observations whereas the inclusion of these processes improves predictions. The inclusion of photochemical reactions for higher iodine oxides leads to ozone predictions that are lower than observations, underscoring the need for further refinement of the halogen emissions and chemistry scheme in the model. (Chemical Equation Presented).

A dynamic evaluation of the Community Multiscale Air Quality (CMAQ) modeling system version 5.0.1 was conducted to evaluate the model's ability to predict changes in ozone levels between 2002 and 2005, a time period characterized by emission reductions associated with the EPA's Nitrogen Oxides State Implementation Plan as well as significant reductions in mobile source emissions. Model results for the summers of 2002 and 2005 were compared to simulations from a previous version of CMAQ to assess the impact of model updates on predicted pollutant response. Changes to the model treatment of emissions, meteorology and chemistry had substantial impacts on the simulated ozone concentrations. While the median bias for high summertime ozone decreased in both years compared to previous simulations, the observed decrease in ozone from 2002 to 2005 in the eastern US continued to be underestimated by the model. Additional “cross” simulations were used to decompose the model predicted change in ozone into the change due to emissions, the change due to meteorology and any remaining change not explained individually by these two components. The decomposition showed that the emission controls led to a decrease in modeled high summertime ozone close to twice as large as the decrease attributable to changes in meteorology alone. Quantifying the impact of retrospective emission controls by removing the impacts of meteorology during the control period can be a valuable approach for communicating to policy makers the net benefit of national control measures.

The U.S. Environmental Protection Agency provides guidelines on the use of air quality models for projecting whether an emission reduction strategy will lead to future pollutant levels that are at or below the National Ambient Air Quality Standards (NAAQS). The EPA's guidance document for ozone attainment demonstrations recommends an attainment test for the 8-h ozone NAAQS based on using the ratio of output from “future” and “base” model simulations through the calculation of location-specific Relative Response Factors (RRF). The 2007 guidance document as well as other related studies have recommended the use of retrospective evaluation studies in order to evaluate the ability of an air quality model to represent a change in air quality (dynamic evaluation) rather than relying solely on operational evaluation of model predictions under base line conditions. Here simulations from the Community Multiscale Air Quality (CMAQ) modeling system were conducted for 2002 and 2005, a time period characterized by significant emissions reductions associated with the EPA's Nitrogen Oxides State Implementation Plan Call (NOx SIP Call) as well as mobile sources. These simulations were used to evaluate the performance of different forms of the RRF metric for projecting 2002 to 2005 against 2005 observed ozone levels. The evaluation study showed that the current form of the RRF calculation is generally well designed for predicting the future 8-hr ozone “design value” metric used for determining attainment. Specifically, the methodology of using air quality model simulations in a relative sense provided better estimates of future ozone design values than using the modeled future year simulation alone. Alternative forms of the RRF metric were found to be very similar to the current methodology in terms of evaluation metrics. However, alternative RRF metrics were sensitive to the number of days used in the calculation of the RRF. Approaches which used more days in the RRF calculation (relative to the 2007 guidance approach) had slightly higher bias and error in predicting 2005 design values compared to approaches using only a subset of high ozone days.

We incorporated oceanic emissions and atmospheric chemistry of dimethylsulfide (DMS) into the hemispheric Community Multiscale Air Quality model and performed annual model simulations without and with DMS chemistry. The model without DMS chemistry predicts higher concentrations of sulfate \(\left( {{\text{SO}}_{4}^{2 - } } \right)\) over land compared to the low concentrations over seawater. Including DMS chemistry substantially increases \(\left( {{\text{SO}}_{4}^{2 - } } \right)\) concentrations over seawater and many coastal areas. It enhances the annual mean surface \(\left( {{\text{SO}}_{4}^{2 - } } \right)\) by >0.8 μg/m3 in some areas of the Pacific Ocean, Atlantic Ocean, Arabian Sea, and Caribbean Sea. The largest enhancement occurs in summer in which DMS chemistry increases surface \({\text{SO}}_{4}^{2 - }\) by 70% over seawater. The model without DMS chemistry underestimates the summer-time observed \({\text{SO}}_{4}^{2 - }\) in the U.S. while the model with DMS chemistry improves model performance in the U.S. coastal areas.

A new version of the Community Multiscale Air Quality (CMAQ) model, version 5.2 (CMAQv5.2), is currently being developed, with a planned release date in 2017. The new model includes numerous updates from the previous version of the model (CMAQv5.1). Specific updates include a new windblown dust scheme; updates to the organic aerosol treatment; updates to the atmospheric chemistry, including the Carbon-Bond 6 chemical mechanism; and various updates to the cloud treatment in the model. In addition, a new lightning assimilation scheme has been implemented in WRF, the meteorological driver for the CMAQ simulations, which greatly improves the placement and intensity of precipitation, which in turn results in improved CMAQ performance. Comparisons between CMAQv5.1 and v5.2 show that ozone (O3) mixing ratios generally increase in the summer with CMAQv5.2, which results in increased bias, while fine particulate matter (PM2.5) concentrations also increase in the summer, which results in decreased bias.

In the middle and upper troposphere, lightning is the most important source of nitrogen oxides (NOX = NO + NO2), which play an essential role in the production of ozone (O3) and influence the oxidizing capacity of the troposphere (Murray 2016). Despite much effort in both observing and modeling lightning NOX during the past decade, considerable uncertainties still exist with the quantification of lightning NOX (LTNOX) production and distribution in the troposphere. Further, it is challenging for regional chemistry and transport models to parameterize LTNOX production and distribution in time and space accurately. Most studies estimate global LTNOX production ranging from 2 to 8 Tg (N) year−1 or about 10–15% of the total NOx budget (Pickering et al. 2014). However, owing to the concerted effort to reduce anthropogenic NOx emissions in recent decades, it is expected that the relative burden of LTNOX and its associated impact on atmospheric chemistry will increase. As a result, it is important to include LTNOX even when modeling ground level air quality and the interaction of air-surface exchange processes.

Due to the computational cost of running regional-scale numerical air quality models, reduced form models (RFM) have been proposed as computationally efficient simulation tools for characterizing the pollutant response to many different types of emission reductions. The U.S. Environmental Protection Agency has developed two types of reduced form models based upon simulations of the Community Multiscale Air Quality (CMAQ) modeling system. One is based on statistical response surface modeling (RSM) techniques using a multidimensional kriging approach to approximate the nonlinear chemical and physical processes. The second approach is based on using sensitivity coefficients estimated with the Decoupled Direct Method in 3 dimensions (CMAQ-DDM-3D) in a Taylor series approximation for the nonlinear response of the pollutant concentrations to changes in emissions from specific sectors and locations. Both types of reduced form models are used to estimate the changes in O 3 and PM 2.5 across space associated with emission reductions of NO x and SO 2 from power plants and other sectors in the eastern United States. This study provides a direct comparison of the RSM- and DDM-3D-based tools in terms of: computational cost, model performance against brute force runs, and model response to changes in emission inputs. For O 3 , the DDM-3D RFM had slightly better performance on average for low to moderate emission cuts compared to the kriging-based RSM, but over-predicted O 3 disbenefits from cuts to mobile source NO x in very urban areas. The RSM approach required more up-front computational cost and produced some spurious O 3 increases in response to reductions in power plant emissions. However the RSM provided more accurate predictions for PM 2.5 and for predictions of very large emission cuts ( e.g. −60 to −90%). This comparison indicates that there are some important differences in the output of the two approaches that should be taken under consideration when interpreting results for a given application.

Complex computer models play a crucial role in air quality research. These models are used to evaluate potential regulatory impacts of emission control strategies and to estimate air quality in areas without monitoring data. For both of these purposes, it is important to calibrate model output with monitoring data to adjust for model biases and improve spatial prediction. In this article, we propose a new spectral method to study and exploit complex relationships between model output and monitoring data. Spectral methods allow us to estimate the relationship between model output and monitoring data separately at different spatial scales, and to use model output for prediction only at the appropriate scales. The proposed method is computationally efficient and can be implemented using standard software. We apply the method to compare Community Multiscale Air Quality (CMAQ) model output with ozone measurements in the United States in July 2005. We find that CMAQ captures large-scale spatial trends, but has low correlation with the monitoring data at small spatial scales.

The efficacy of standard air quality model evaluation techniques is becoming compromised as the simulation periods continue to lengthen in response to ever increasing computing capacity. Accordingly, the purpose of this paper is to demonstrate a statistical approach called Principal Component Analysis (PCA) with the intent of motivating its use by the evaluation community. One of the main objectives of PCA is to identify, through data reduction, the recurring and independent modes of variations (or signals) within a very large dataset, thereby summarizing the essential information of that dataset so that meaningful and descriptive conclusions can be made. In this demonstration, PCA is applied to a simple evaluation metric – the model bias associated with EPA's Community Multi-scale Air Quality (CMAQ) model when compared to weekly observations of sulfate (SO 4 2− ) and ammonium (NH 4 + ) ambient air concentrations measured by the Clean Air Status and Trends Network (CASTNet). The advantages of using this technique are demonstrated as it identifies strong and systematic patterns of CMAQ model bias across a myriad of spatial and temporal scales that are neither constrained to geopolitical boundaries nor monthly/seasonal time periods (a limitation of many current studies). The technique also identifies locations (station–grid cell pairs) that are used as indicators for a more thorough diagnostic evaluation thereby hastening and facilitating understanding of the probable mechanisms responsible for the unique behavior among bias regimes. A sampling of results indicates that biases are still prevalent in both SO 4 2− and NH 4 + simulations that can be attributed to either: 1) cloud processes in the meteorological model utilized by CMAQ, which are found to overestimated convective clouds and precipitation, while underestimating larger-scale resolved clouds that are less likely to precipitate, and 2) biases associated with Midwest NH 3 emissions which may be partially ameliorated using the bi-directional NH 3 exchange option in CMAQ.

Regional air quality models are being used in a policy-setting to assess the changes in air pollutant concentrations from changes in emissions and meteorology. Dynamic evaluation entails examination of a retrospective case(s) to examine whether an air quality model has properly predicted the air quality response to known changes in emissions and/or meteorology. In this study, the Kolmogorov–Zurbenko (KZ) filter has been used to spectrally decompose pollutant time series into different forcings that are controlled by different atmospheric processes influencing the predicted and observed pollutant concentrations. Through analyses of the different components influenced by different forcings as part of dynamic model evaluation, we can discern which of the component(s) or scale(s) of forcing are simulated well by the model and the component(s) or scale(s) of forcing needing further improvement in the model. The KZ filter has been applied to both the observed and Community Multiscale Air Quality (CMAQ) model-predicted summertime ozone (O3) time series in years 2002 and 2005. The 2002–2005 time period is a good candidate for the dynamic evaluation case study because of the large changes in NOx emissions as a result of the U.S. Environmental Protection Agency's (USEPA) NOx State Implementation Plan (SIP) call together with a gradual decline in mobile emissions. Results suggest that the CMAQ model performs similarly for both years in terms of capturing the observed synoptic-scale forcing. However, the changes in the observed ozone baseline component (i.e. longer-term variations) are not properly captured by the model at some locations. The factors contributing to the ozone baseline include emissions loading, boundary conditions, and other parameters that vary slowly over time. Analysis using a reduced form model developed from the sensitivity coefficients calculated from the decoupled direct method in three dimensions (DDM-3D) reveals that ground-level NOx emissions, especially those from mobile and area source sectors, may be overestimated in 2005 as there is an increase in the mean bias for the 2005 ozone simulations from its 2002 level at the majority of AQS sites located within the states affected by the NOx SIP Call.

This study develops probabilistic estimates of ozone (O3) sensitivities to precursor emissions by incorporating uncertainties in photochemical modeling and evaluating model performance based on ground-level observations of O3 and oxides of nitrogen (NOx). Uncertainties in model formulations and input parameters are jointly considered to identify factors that strongly influence O3 concentrations and sensitivities in the Dallas-Fort Worth region in Texas. Weightings based on a Bayesian inference technique and screenings based on model performance and statistical tests of significance are used to generate probabilistic representation of O3 response to emissions and model input parameters. Adjusted (observation-constrained) results favor simulations using the sixth version of the carbon bond chemical mechanism (CB6) and scaled-up emissions of NOx, dampening the overall sensitivity of O3 to NOx and increasing the sensitivity of O3 to volatile organic compounds in the study region. This approach of using observations to adjust and constrain model simulations can provide probabilistic representations of pollutant responsiveness to emission controls that complement the results obtained from deterministic air-quality modeling.

The Community Multiscale Air Quality (CMAQ) model is a comprehensive multi-pollutant air quality modeling system developed and maintained by the U.S. Environmental Protection Agency's (EPA) Office of Research and Development (ORD). Recently, version 5.1 of the CMAQ model (v5.1) was released to the public which incorporates a large number of science updates and extended capabilities over the previous release version of the model (v5.0.2). These updates include improvements in the meteorological calculations in both CMAQ and the Weather Research and Forecast (WRF) model used to provide meteorological fields to CMAQ; updates to the gas and aerosol chemistry; revisions to the calculations of clouds and photolysis; and improvements to the dry and wet deposition in the model. Sensitivity simulations isolating several of the major updates to the modeling system show that changes to the meteorological calculations generally result in greater afternoon and early evening mixing in the model, times when the model historically underestimates mixing. The result is higher ozone (O3) mixing ratios on average due to reduced NO titration and lower fine particulate matter (PM2.5) concentrations due to greater dilution of primary pollutants (e.g. elemental and organic carbon). Updates to the clouds and photolysis calculations greatly improve consistency between the WRF and CMAQ models and result in generally higher O3 mixing ratios, primarily due to reduced cloudiness and reduced attenuation of photolysis in the model. Updates to the aerosol chemistry results in higher secondary organic aerosol (SOA) concentrations in the summer, thereby reducing PM2.5 bias, while updates to the gas chemistry result in generally increased O3 in January and July (small) and slightly higher PM2.5 concentrations on average in both January and July. Overall, seasonal variation in simulated PM2.5 generally improves in the new model version, as concentrations decrease in the winter (when PM2.5 is overestimated by CMAQ v5.0.2) and increase in the summer (when PM2.5 is underestimated by CMAQ v5.0.2). Ozone mixing ratios are higher on average with v5.1 versus v5.0.2, resulting in higher O3 mean bias, as O3 tends to be overestimated by CMAQ throughout most of the year (especially at locations where the observed O3 is low), however both the error and correlation are largely improved with v5.1. Sensitivity simulations for several hypothetical emission reduction scenarios showed that v5.1 tends to be slightly more responsive to reductions in NOx (NO + NO2), VOC and SOx (SO2 + SO4) emissions than v5.0.2, representing an improvement as previous studies have shown CMAQ to underestimate the observed reduction in O3 due to large, widespread reductions in observed emissions. Finally, the computational efficiency of the model was significantly improved in v5.1, which keeps runtimes similar to v5.0.2 despite the added complexity to the model.

Numerical air quality models are being used for assessing emission control strategies for improving ambient pollution levels across the globe. This paper applies probabilistic modeling to evaluate the effectiveness of emission reduction scenarios aimed at lowering ground-level ozone concentrations. A Bayesian hierarchical model is used to combine air quality model output and monitoring data in order to characterize the impact of emissions reductions while accounting for different degrees of uncertainty in the modeled emissions inputs. The probabilistic model predictions are weighted based on population density in order to better quantify the societal benefits/disbenefits of four hypothetical emission reduction scenarios in which domain-wide NO(x) emissions from various sectors are reduced individually and then simultaneously. Cross validation analysis shows the statistical model performs well compared to observed ozone levels. Accounting for the variability and uncertainty in the emissions and atmospheric systems being modeled is shown to impact how emission reduction scenarios would be ranked, compared to standard methodology.

VA National Center for PTSD; VA Boston Healthcare System, Boston, MA; Boston UniversitySchool of MedicineObjective: The commission of wartime atrocities is a phenomenon known to occuramong military personnel deployed to war zones. Although some research hasexamined the psychological sequelae to these events, there is virtually no researchon the causes or correlates of such behavior. The present study examined associ-ations among personality variables, combat exposure, and witnessing or participat-ing in wartime atrocities during the Vietnam War. Method: One thousand onehundred and four Army and Marine veterans of the Vietnam War completedself-report measures that assessed personality, combat exposure, and participationin atrocities. Structural equation modeling was used to test a model in whichcombat exposure mediated the association between personality and participation inatrocities. Results: Combat exposure was strongly associated with self-reportedparticipation in atrocities and fully mediated the association between trait aggres-sion and participation in atrocities. Combat exposure was also shown to partiallymediate the association between trait neuroticism and atrocities but evidenced norelationship with trait disconstraint. Disconstraint showed only a direct associationwith participation in atrocities. Conclusions: Personality and exposure to heavycombat should be considered not only risk factors for physical or mental illness, butalso important risk factors for the commission of atrocities. Longitudinal researchon risk factors for the commission of atrocities is needed, with a focus on thoseservice members tasked with more dangerous assignments. Military commandersmay wish to pay close attention to units under greater combat stress and to continueto devote resources to screening and treatment of members of these units.Keywords

Although most individuals with posttraumatic stress disorder (PTSD) demonstrate heightened physiological reactivity to trauma-related cues, many of these individuals do not. The presence of comorbid externalizing disorders is a potential explanation for this inconsistency. This study investigated the psychophysiological reactions to both standardized and idiographic trauma-related cues among male Vietnam Veterans with PTSD only, PTSD and a comorbid substance use disorder (PTSD-SUD), PTSD and comorbid antisocial personality disorder (PTSD-ASPD), PTSD and both comorbid ASPD and SUD (PTSD-ASPD/SUD), and healthy controls. Results showed that the heart rate reactivity of the PTSDASPD and PTSD-ASPD/SUD groups failed to exceed that of the No Disorder group during the imagery-based task, and the PTSD-ASPD/SUD group showed less skin conductance reactivity than the other three PTSD groups in response to the standardized trauma cues. These findings implicate ASPD comorbidity in reduced physiological reactivity to trauma reminders in some individuals with PTSD.