Regional Photochemical Model Research Articles

A hyperlocal hybrid data fusion near-road PM2.5 and NO2 annual risk and environmental justice assessment across the United States.

Exposure to traffic-related air pollutants (TRAPs) has been associated with numerous adverse health effects. TRAP concentrations are highest meters away from major roads, and disproportionately affect minority (i.e., non-white) populations often considered the most vulnerable to TRAP exposure. To demonstrate an improved assessment of on-road emissions and to quantify exposure inequity in this population, we develop and apply a hybrid data fusion approach that utilizes the combined strength of air quality observations and regional/local scale models to estimate air pollution exposures at census block resolution for the entire U.S. We use the regional photochemical grid model CMAQ (Community Multiscale Air Quality) to predict the spatiotemporal impacts at local/regional scales, and the local scale dispersion model, R-LINE (Research LINE source) to estimate concentrations that capture the sharp TRAP gradients from roads. We further apply the Regionalized Air quality Model Performance (RAMP) Hybrid data fusion technique to consider the model's nonhomogeneous, nonlinear performance to not only improve exposure estimates, but also achieve significant model performance improvement. With a R2 of 0.51 for PM2.5 and 0.81 for NO2, the RAMP hybrid method improved R2 by ~0.2 for both pollutants (an increase of up to ~70% for PM2.5 and ~31% NO2). Using the RAMP Hybrid method, we estimate 264,516 [95% confidence interval [CI], 223,506-307,577] premature deaths attributable to PM2.5 from all sources, a ~1% overall decrease in CMAQ-estimated premature mortality compared to RAMP Hybrid, despite increases and decreases in some locations. For NO2, RAMP Hybrid estimates 138,550 [69,275-207,826] premature deaths, a ~19% increase (22,576 [11,288 - 33,864]) compared to CMAQ. Finally, using our RAMP hybrid method to estimate exposure inequity across the U.S., we estimate that Minorities within 100 m from major roads are exposed to up to 15% more PM2.5 and up to 35% more NO2 than their White counterparts.

Comparison of different hybrid modeling methods to estimate intraurban NO2 concentrations

Exposure to air pollution has a significant impact on the health of urban populations, so the improvement of methods that model the concentrations of air pollutants within complex urban areas is important in health studies to adequately asses the exposure of the population. This paper presents several hybrid, high-resolution models to simulate the variability of ambient NO2 concentrations in Seoul, the capital of South Korea. These models combine the Community Multiscale Quality (CMAQ) as a regional photochemical model with a fine scale model of either the California Puff dispersion model (CALPUFF) or the land use regression model (LUR). We compared high-resolution estimates of the spatial NO2 concentration from four different hybrid models, including 1) raw CMAQ-CALPUFF; 2) observation-fused CMAQ-CALPUFF; 3) raw CMAQ-LUR; and 4) observation-fused CMAQ-LUR. We conducted numerical simulations of the NO2 concentrations during the winter season and compared the results with field data obtained from mobile measurements captured from December 2017 to February 2018. The results indicate that observation-fused hybrid models offered improved agreement with the mobile measurements: for the CMAQ-CALPUFF model, statistical bias and error were reduced to about 82% and 57%, respectively by using observation-fused CMAQ. We also found significant differences in the sub-grid variability of the NO2 concentrations for the different hybrid models. The predictions obtained with CMAQ-CALPUFF showed concentrations that were more widely distributed (1.7 and 1.4 times for the 10–90th range, observation-fused case) when compared to the only-CMAQ and CMAQ-LUR predictions, respectively. Our study suggests that a properly evaluated hybrid model can increase the predictive accuracy of air pollutant concentration in complex urban areas to improve exposure assessments in health studies.

Air quality and health benefits from potential coal power plant closures in Texas

ABSTRACTAs power production from renewable energy and natural gas grows, closures of some coal-fired power plants in Texas become increasingly likely. In this study, the potential effects of such closures on air quality and human health were analyzed by linking a regional photochemical model with a health impacts assessment tool. The impacts varied significantly across 13 of the state’s largest coal-fired power plants, sometimes by more than an order of magnitude, even after normalizing by generation. While some power plants had negligible impacts on concentrations at important monitors, average impacts up to 0.5 parts per billion (ppb) and 0.2 µg/m3 and maximum impacts up to 3.3 ppb and 0.9 µg/m3 were seen for ozone and fine particulate matter (PM2.5), respectively. Individual power plants impacted average visibility by up to 0.25 deciviews in Class I Areas. Health impacts arose mostly from PM2.5 and were an order of magnitude higher for plants that lack scrubbers for SO2. Rankings of health impacts were largely consistent across the base model results and two reduced form models. Carbon dioxide emissions were relatively uniform, ranging from 1.00 to 1.26 short tons/MWh, and can be monetized based on a social cost of carbon. Despite all of these unpaid externalities, estimated direct costs of each power plant exceeded wholesale power prices in 2016.Implications: While their CO2 emission rates are fairly similar, sharply different NOx and SO2 emission rates and spatial factors cause coal-fired power plants to vary by an order of magnitude in their impacts on ozone, particulate matter, and associated health and visibility outcomes. On a monetized basis, the air pollution health impacts often exceed the value of the electricity generated and are of similar magnitude to climate impacts. This suggests that both air pollution and climate should be considered if externalities are used to inform decision making about power-plant dispatch and retirement.

Comparison of Different Hybrid Modeling Methods for the Estimation of Intraurban Air Pollutant Concentrations

Exposure to air pollution – a major potential environmental factor – can exert significant health effects on the urban population. The improvement of modeling methods to estimate the concentrations of air pollutants within a complex city should be important for exposure assessment of subjects in health studies. This paper presents several hybrid modeling approaches to simulate highly resolved variability in ambient air pollution in the capital of South Korea, Seoul. They combine the Community Multiscale Quality (CMAQ), a regional photochemical model with fine scale models including the California Puff (CALPUFF) dispersion model and the land use regression (LUR) model. We compared the high-resolution spatial pollutant concentration estimates from four different hybrid combinations: 1) raw CMAQ-CALPUFF; 2) observation-fused CMAQ-CALPUFF; 3) raw CMAQ-LUR; 4) observation-fused CMAQ-LUR. We quantitively evaluated the simulated concentrations with field data from mobile measurements carried out during the winter season. The results showed that significant differences in sub-grid variability of pollutant concentrations were found according to different hybrid modeling methods and observation-fused hybrid modeling can generally improve the model performance in a complex urban area. Our study suggests that a properly evaluated hybrid modeling approach could increase the accuracy of air pollutant concentration estimates for the purpose of improving exposure assessment in a health study.

Nonlinear data assimilation for the regional modeling of maximum ozone values.

We present a new method of data assimilation with the aim of correcting the forecast of the maximum values of ozone in regional photo-chemical models for areas over complex terrain using multilayer perceptron artificial neural networks. Up until now, these types of models have been used as a single model for one location when forecasting concentrations of air pollutants. We propose a method for constructing a more ambitious model: a single model, which can be used at several locations because the model is spatially transferable and is valid for the whole 2D domain. To achieve this goal, we introduce three novel ideas. The new method improves correlation at measurement station locations by 10% on average and improves by approximately 5% elsewhere.

Methods for reducing biases and errors in regional photochemical model outputs for use in emission reduction and exposure assessments

In the United States, regional-scale photochemical models are being used to design emission control strategies needed to meet the relevant National Ambient Air Quality Standards (NAAQS) within the framework of the attainment demonstration process. Previous studies have shown that the current generation of regional photochemical models can have large biases and errors in simulating absolute levels of pollutant concentrations. Studies have also revealed that regional air quality models were not always accurately reproducing even the relative changes in ozone air quality stemming from changes in emissions. This paper introduces four approaches to adjust for model bias and errors in order to provide greater confidence for their use in estimating future concentrations as well as using modeled pollutant concentrations in exposure assessments. The four methods considered here are a mean and variance (MV) adjustment, temporal component decomposition (TC) adjustment of modeled concentrations, and two variants of cumulative distribution function (CDF) mapping. These methods were compared against each other as well as against unadjusted model concentrations and a version of the relative response approach based on unadjusted model predictions. The analysis uses ozone concentrations simulated by the Community Multiscale Air Quality (CMAQ) model for the northeastern United States domain for the years 1996–2005. Ensuring that base case conditions are adequately represented through the combined use of observations and model simulations is shown to result in improved estimates of future air quality under changing emissions and meteorological conditions.

Photochemical production of CO and CO2 in the Northern Gulf of Mexico: Estimates and challenges for quantifying the impact of photochemistry on carbon cycles

The photochemical production of carbon dioxide (CO2) and carbon monoxide (CO), derived from marine colored dissolved organic matter (CDOM), is considered a significant oceanic loss process for the large and variable fluxes of terrigenous dissolved organic carbon (DOC) in river dominated coastal systems. Using samples collected over 4 seasons (2009–2010) in a grid centered on the Mississippi–Atchafalaya River system in the Northern Gulf of Mexico, we attempt to improve constraints for these direct photochemical impacts on DOC cycles by contributing the largest coherent set of photochemical apparent quantum yield (AQY) spectra for CO to date (n=99), 18 of which had CO2 AQY spectra determined in the same sample for use in calculating CO2:CO photoproduction ratios. Simple correlations, previously reported from much smaller data sets, between CO photoproduction and CDOM optical properties showed weak or no correlations within our much larger spatio-temporal study. However, grouping samples with an optical transition point (ag(320)=1.3m−1) allowed definition of two distinct inshore and offshore CO AQY spectra. Inputting these in regional photochemical models that use remotely sensed ocean color data and modeled water optical properties dramatically improves results over using a single CO AQY spectrum (modeled vs. measured r2=0.73 vs. 0.18 for single AQY), as has been done previously. Monthly average CO photoproduction rates ranged from 6.0 to 17.7μmolm−2d−1, amounting to a conservative estimate of 3.35GgCyr−1 for our study region. Our data also shows that the efficiency for CO2 photoproduction decreases over prolonged irradiations up to 48h. Consequently, no assessable method exists for direct determination of initial rates in clear waters with low CO2 photoproduction. Therefore CO2 photoefficiency in the Gulf of Mexico was inferred using CO photoproduction estimates and CO2:CO ratios. In agreement with previous studies, CO2:CO ratios determined here were also poorly constrained, ranging from ~6 to 66 providing a median value of 24.4. This approach estimates that direct photochemical production of CO2 plus CO can remineralize DOC on the order of 85GgC each year. This estimate provides an improvement over the use of a single inshore CO2 AQY spectrum for a largely blue water system but caution should be used when interpreting CO2 photochemical flux estimates that rely heavily on the selection of CO AQY spectra and/or poorly constrained relationships between CO2 and CO photoproduction. Our results stress that the continued expansion of the CO2:CO ratio database to new oceanic regimes is likely of very limited value and that assessable direct methods or better proxies to quantify CO2 photochemistry in clear marine waters are needed.

Simultaneous statistical bias correction of multiple PM2.5 species from a regional photochemical grid model

In recent years environmental epidemiologists have begun utilizing regional-scale air quality computer models to predict ambient air pollution concentrations in health studies instead of or in addition to data from fixed-site ambient monitors. The advantages of using such models include better spatio-temporal coverage and the capability to predict concentrations of unmonitored pollutants. However, there are also drawbacks, chief among them being that these models can exhibit systematic spatial and temporal biases. In order to use these models in epidemiological investigations it is very important to bias-correct the model surfaces. We present a novel statistical method of spatio-temporal bias correction for the Community Multi-scale Air Quality (CMAQ) model that allows simultaneous bias adjustment of PM2.5 mass and its major constituent species using publically available speciated data from ambient monitors. The method uses mass conservation and the more widespread unspeciated PM2.5 mass observations to constrain the sum of the PM2.5 species' concentrations in locations without speciated monitors. We develop the model in the context of an epidemiological study investigating the association between PM2.5 species’ ambient concentrations and birth outcomes throughout the state of New Jersey. Since our exposures of interest are multi-month averages we focus specifically on modeling seasonal bias trends rather than daily biases. Using a cross-validation study we find that our bias-corrected CMAQ results are more accurate than either the original CMAQ output or a spline fit without CMAQ. More interestingly, we find that our model clearly performs better when mass conservation is enforced, and furthermore that our model is competitive with Kriging in a comparison in which the latter has the advantage.

Extreme value analysis for evaluating ozone control strategies

Tropospheric ozone is one of six criteria pollutants regulated by the US EPA, and has been linked to respiratory and cardiovascular endpoints and adverse effects on vegetation and ecosystems. Regional photochemical models have been developed to study the impacts of emission reductions on ozone levels. The standard approach is to run the deterministic model under new emission levels and attribute the change in ozone concentration to the emission control strategy. However, running the deterministic model requires substantial computing time, and this approach does not provide a measure of uncertainty for the change in ozone levels. Recently, a reduced form model (RFM) has been proposed to approximate the complex model as a simple function of a few relevant inputs. In this paper, we develop a new statistical approach to make full use of the RFM to study the effects of various control strategies on the probability and magnitude of extreme ozone events. We fuse the model output with monitoring data to calibrate the RFM by modeling the conditional distribution of monitoring data given the RFM using a combination of flexible semiparametric quantile regression for the center of the distribution where data are abundant and a parametric extreme value distribution for the tail where data are sparse. Selected parameters in the conditional distribution are allowed to vary by the RFM value and the spatial location. Also, due to the simplicity of the RFM, we are able to embed the RFM in our Bayesian hierarchical framework to obtain a full posterior for the model input parameters, and propagate this uncertainty to the estimation of the effects of the control strategies. We use the new framework to evaluate three potential control strategies, and find that reducing mobile-source emissions has a larger impact than reducing point-source emissions or a combination of several emission sources.

A wavelet-based approach to blending observations with deterministic computer models to resolve the intraurban air pollution field

Recent interest in near-road exposure to air pollutants and related health and environmental justice issues has highlighted the importance of improving the accuracy of intraurban ambient concentration estimates. Unfortunately, except in rare cases, no single source of information can accurately estimate the concentration at the desired spatial and temporal resolution over the full time period of epidemiological interest. However, it is possible to blend information from several sources so as to exploit the strengths and offset the weaknesses of each. Specifically, we are interested in combining data from ambient monitors with output from deterministic air pollution computer models. Monitor networks are sparse in both space and time, are costly to maintain, and are usually designed expressly to avoid detecting local-scale features. We use two types of computer models to compensate for these drawbacks. The first, a grid-based regional photochemical model, Community Multiscale Air Quality (CMAQ), covers large areas at high time resolution but cannot resolve features smaller than a grid cell, usually 4, 12, or 36 km across. The second, a plume dispersion model, AMS/EPA Regulatory Model (AERMOD), can resolve these features but cannot track long-distance transport or chemical reactions. We present a new Bayesian method that combines these three sources of information to resolve the intraurban pollution field. This method represents the true latent field using a two-dimensional wavelet basis, which allows direct, efficient incorporation of data at multiple levels of resolution. It furthermore allows a priori selection of the relative importance of each data source. We test its predictive accuracy and precision in a realistic urban-scale simulation. Finally, in the context of two air pollution health studies in Atlanta, Georgia, we use our model to estimate the daily mean concentrations of oxides of nitrogen (NOx), particulate matter with an aerodynamic diameter ≤2.5 μm (PM2.5), and carbon monoxide (CO) at a mixture of census block group and zip code centroids for the years 2001–2002. Implications: This paper describes a novel method of blending regional and local model results with observations to produce accurate air pollutant concentrations at the desired spatial and temporal resolution over urban areas to support environmental epidemiological studies.

Modeling analysis of the impact of anthropogenic emission sources on ozone concentration over selected urban areas in Texas

Due to several regional–scale high ozone episodes that impacted the urban areas in south and central Texas over the past decade, the Texas Commission on Environmental Quality (TCEQ) has designated several urban regions as near non–attainment areas. A regional photochemical modeling experiment was set up to simulate a high ozone episode of September 11–14, 2002 in order to evaluate the impact of various anthropogenic emissions sources on ozone concentrations. The base case simulation showed reasonable model performance by capturing the peaks and the diurnal variability of observed ozone concentrations within the modeling domain. A comprehensive impact assessment of anthropogenic emissions from various source categories to the 8–hour ozone concentration was evaluated for each of the urban areas within the study region. Through a source apportionment analysis of emissions influencing the 8– hour ozone concentrations, NOx and VOC limited areas were identified. The model results showed that the net effect of all anthropogenic emissions was approximately 8.4 ppb in Victoria, 8.8 ppb in Corpus Christi, and 31.2 and 34.1 ppb in Austin and San Antonio, respectively. Impact of major emissions source categories differed regionally with ozone concentrations in Austin and San Antonio mainly influenced by mobile sources, while Corpus Christi and Victoria were largely impacted by long–range transport of ozone. On a local scale, Corpus Christi was also impacted by non–road sources, while Victoria was influenced by point sources. Ozone sensitivity analysis showed higher sensitivity towards VOC within the urban cores of Austin, San Antonio, and Corpus Christi, while the overall modeling regions showed higher NOx sensitivity. This would indicate that both NOx and VOC emissions reduction plans need to be developed and implemented for the mitigation of regional and urban ozone. The results also revealed that biogenic emissions played an important role in the urban regions of south and central Texas.

Influence of air quality model resolution on uncertainty associated with health impacts

Abstract. We use regional air quality modeling to evaluate the impact of model resolution on uncertainty associated with the human health benefits resulting from proposed air quality regulations. Using a regional photochemical model (CAMx), we ran a modeling episode with meteorological inputs simulating conditions as they occurred during August through September 2006 (a period representative of conditions leading to high ozone), and two emissions inventories (a 2006 base case and a 2018 proposed control scenario, both for Houston, Texas) at 36, 12, 4 and 2 km resolution. The base case model performance was evaluated for each resolution against daily maximum 8-h averaged ozone measured at monitoring stations. Results from each resolution were more similar to each other than they were to measured values. Population-weighted ozone concentrations were calculated for each resolution and applied to concentration response functions (with 95% confidence intervals) to estimate the health impacts of modeled ozone reduction from the base case to the control scenario. We found that estimated avoided mortalities were not significantly different between the 2, 4 and 12 km resolution runs, but the 36 km resolution may over-predict some potential health impacts. Given the cost/benefit analysis requirements motivated by Executive Order 12866 as it applies to the Clean Air Act, the uncertainty associated with human health impacts and therefore the results reported in this study, we conclude that health impacts calculated from population weighted ozone concentrations obtained using regional photochemical models at 36 km resolution fall within the range of values obtained using fine (12 km or finer) resolution modeling. However, in some cases, 36 km resolution may not be fine enough to statistically replicate the results achieved using 2, 4 or 12 km resolution. On average, when modeling at 36 km resolution, an estimated 5 deaths per week during the May through September ozone season are avoided because of ozone reductions resulting from the proposed emissions reductions (95% confidence interval was 2–8). When modeling at 2, 4 or 12 km finer scale resolution, on average 4 deaths are avoided due to the same reductions (95% confidence interval was 1–7). Study results show that ozone modeling at a resolution finer than 12 km is unlikely to reduce uncertainty in benefits analysis for this specific region. We suggest that 12 km resolution may be appropriate for uncertainty analyses of health impacts due to ozone control scenarios, in areas with similar chemistry, meteorology and population density, but that resolution requirements should be assessed on a case-by-case basis and revised as confidence intervals for concentration-response functions are updated.

Observation and modeling of the evolution of Texas power plant plumes

Abstract. During the second Texas Air Quality Study 2006 (TexAQS II), a full range of pollutants was measured by aircraft in eastern Texas during successive transects of power plant plumes (PPPs). A regional photochemical model is applied to simulate the physical and chemical evolution of the plumes. The observations reveal that SO2 and NOy were rapidly removed from PPPs on a cloudy day but not on the cloud-free days, indicating efficient aqueous processing of these compounds in clouds. The model reasonably represents observed NOx oxidation and PAN formation in the plumes, but fails to capture the rapid loss of SO2 (0.37 h−1) and NOy (0.24 h−1) in some plumes on the cloudy day. Adjustments to the cloud liquid water content (QC) and the default metal concentrations in the cloud module could explain some of the SO2 loss. However, NOy in the model was insensitive to QC. These findings highlight cloud processing as a major challenge to atmospheric models. Model-based estimates of ozone production efficiency (OPE) in PPPs are 20–50 % lower than observation-based estimates for the cloudy day.

Regional scale photochemical model evaluation of total mercury wet deposition and speciated ambient mercury

Methylmercury is a known neurotoxin with deleterious health effects on humans and wildlife. Atmospheric deposition is the largest source of mercury loading to most terrestrial and aquatic ecosystems. Regional scale air quality models are needed to quantify mercury deposition resulting from complex emissions sources and physical and chemical processes that govern the fate of mercury in the atmosphere. Total mercury wet deposition estimates from multiple regional photochemical transport models applied at 12 km grid resolution over the continental United States compare well with observations (CAMx fractional error = 45%, CMAQ fractional error = 33%) despite uncertainties in global mercury emissions inventories and certain chemical transformation pathways. In addition, both CMAQ and CAMx well represent observed diel and seasonal patterns of Hg(0) and tend to exaggerate the diel patter of Hg(II) at AMNet monitor locations. The observed fraction of particulate mercury to total oxidized mercury (sum of particulate mercury and Hg(II)) is generally greater in colder months and during overnight hours. The modeling systems tend to capture these patterns but have a systematically lower fraction of particulate mercury to total oxidized mercury, especially in winter months. Annual total mercury deposition from wet and dry processes is 65% greater in CMAQ compared to CAMx over the entire modeling domain. This is largely due to higher wet deposition in CMAQ and higher dry deposition of Hg(0), which is treated as equilibrium with mercury re-emissions and not modeled in CAMx. A sensitivity using CAMx with Hg(0) dry deposition treated similar to CMAQ resulted in more comparable total mercury deposition estimates. Modeled dry deposition velocities for Hg(II) compare well with the limited experimental data, while Hg(0) dry deposition velocities are lower than published experimental data. A mercury bi-directional flux sensitivity application in CMAQ had the overall effect of reducing total mercury dry deposition and slightly improving ambient Hg(0) performance. The range of the domain wide total deposition from all model sensitivities was within 25% of the mean but exhibited larger deviations in the individual wet and dry deposition budgets. The contribution of mercury initial conditions and lateral boundary inflow conditions were tracked separately using CAMx source apportionment. Initial contribution to total mercury deposition for the entire model domain falls below 5% after 2 weeks. Boundary contribution to total mercury deposition vary considerably across the continental United States, but ranges between 20 and 99% at MDN monitor locations.

Interpreting predictions from the SAPRC07 mechanism based on regional and continental simulations

Abstract The SAPRC07T mechanism is implemented and evaluated in the CMAQ air quality model. The implementation is described and tested with simulations over the United States for two periods. The evaluation compares results against observations for ozone and particulate matter as well as against predictions from the SAPRC-99 mechanism for the same simulation periods and model domain. Comparisons against SAPRC-99 results reveal largest differences over urban areas with low VOC to NOx ratios. Integrated rate analysis is used to investigate the chemical reactions responsible for these differences. Comparison against observations shows over predictions of ozone concentrations over the southeastern United States. The errors appear linked to failing to match the observations during early morning hours. We discuss the source of the errors in terms of both simulation inputs and the photochemical mechanism and recommend potential directions for improving applications with regional photochemical models.

Impact of Flare Destruction Efficiency and Products of Incomplete Combustion on Ozone Formation in Houston, Texas

The impact of flare destruction removal efficiency (DRE) and Products of Incomplete Combustion (PICs) on ozone formation was examined using a regional photochemical model. Emission scenarios for five industrial flares were considered. For each flare, four DRE values (95, 90, 75, and 50%) were assumed, along with a base case that assumed 98 or 99% DRE. For each DRE level, a scenario assuming that no PICs and a scenario assuming a level of PICs consistent with full scale flare tests was evaluated. Simulation results indicated that low DREs can increase ambient ozone concentrations by more than 15 ppb under some conditions, but under other conditions, may raise ozone concentrations by 1 ppb or less. Emission rates of unburned flare gases and the chemical reactivity of the unburned hydrocarbons explain much of the variability in ozone formation. The air quality simulations also showed that unburned flare gases can have a larger impact on ozone formation than PICs.

Investigation into approaches to reduce excessive vertical transport over complex terrain in a regional photochemical grid model

Past photochemical modeling of the western United States (US) using the Comprehensive Air quality Model with extensions (CAMx) and the Community Multiscale Air Quality (CMAQ) model has resulted in large springtime ozone over predictions in the complex high-elevation terrain of the western United States (US). Comparisons against rural measurement data have shown that both models over predicted ozone levels by 20 ppb or more. A systematic investigation using CAMx revealed that excessive vertical transport in mountainous terrain draws down upper tropospheric ozone introduced by the lateral boundary conditions (developed by a global chemistry model), which can routinely exceed 1 ppm near the tropopause. This is not an unreasonable concentration at such altitudes during the spring, and there is observational evidence that stratospheric ozone intrusions result in occasional large ground-level concentrations in the western US, but not at the frequency and intensity simulated by the CAMx and CMAQ models. Past versions of CAMx and CMAQ possess similar algorithms to diagnose vertical velocity, and similar first-order accurate vertical advection algorithms. These similarities, in conjunction with poor vertical resolution of the upper troposphere, have resulted in similar ozone performance issues. Numerous approaches were explored with CAMx in an attempt to externally reduce the rates of vertical transport over complex terrain. Ultimately, we formulated and tested a new vertical advection methodology that included improvements to how vertical velocities are determined and introduced a second-order accurate advection solver technique. Together these improvements proved to yield the most successful results in reducing upper tropospheric ozone transport to the surface. CAMx was then run to simulate ozone throughout the western US for a full year to evaluate the effects of the new vertical transport algorithm on model performance. Ozone performance improvements exceeded those achieved through the application of arbitrary reductions in the upper tropospheric/stratospheric lateral boundary conditions.

An analysis of long-term regional-scale ozone simulations over the Northeastern United States: variability and trends

Abstract. This study presents the results from two sets of 18-year air quality simulations over the Northeastern US performed with a regional photochemical modeling system. These two simulations utilize different sets of lateral boundary conditions, one corresponding to a time-invariant climatological vertical profile and the other derived from monthly mean concentrations extracted from archived ECHAM5-MOZART global simulations. The objective is to provide illustrative examples of how model performance in several key aspects – trends, intra- and interannual variability of ground-level ozone, and ozone/precursor relationships – can be evaluated against available observations, and to identify key inputs and processes that need to be considered when performing and improving such long-term simulations. To this end, several methods for comparing observed and simulated trends and variability of ground level ozone concentrations, ozone precursors and ozone/precursor relationships are introduced. The application of these methods to the simulation using time-invariant boundary conditions reveals that the observed downward trend in the upper percentiles of summertime ozone concentrations is captured by the model in both directionality and magnitude. However, for lower percentiles there is a marked disagreement between observed and simulated trends. In terms of variability, the simulations using the time-invariant boundary conditions underestimate observed inter-annual variability by 30%–50% depending on the percentiles of the distribution. The use of boundary conditions from the ECHAM5-MOZART simulations improves the representation of interannual variability but has an adverse impact on the simulated ozone trends. Moreover, biases in the global simulations have the potential to significantly affect ozone simulations throughout the modeling domain, both at the surface and aloft. The comparison of both simulations highlights the significant impact lateral boundary conditions can have on a regional air quality model's ability to simulate long-term ozone variability and trends, especially for the lower percentiles of the ozone distribution.

Air Quality Model Evaluation International Initiative (AQMEII): Advancing the State of the Science in Regional Photochemical Modeling and Its Applications

aMErICan METEOrOLOGICaL SOCIETy | 23 AffiliAtions: Rao—Atmospheric Modeling and Analysis Division, National Exposure Research Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina; GalmaRini—Institute of Environment and Sustainability, European Commission, Joint Research Center, Ispra, Italy; Puckett—Air Quality Research Division, Environment Canada, Toronto, Ontario, Canada Corresponding Author: Dr. S. T. Rao, Atmospheric Modeling and Analysis Division, National Exposure Research Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711 E-mail: rao.st@epa.gov

CVPS: An operator solving complex chemical and vertical processes simultaneously with sparse-matrix techniques

Abstract We present a locally, one-dimensional operator that couples complex Chemical and Vertical Physical processes with Sparse-matrix techniques (CVPS) for multi-dimensional regional photochemical transport models. The CVPS operator solves fundamental interaction between chemical reactions and vertical physical processes in the atmospheric boundary layer at each time step, and may be used to simulate chemicals sensitive to both vertical mixing and photochemistry at a time step. The CVPS operator is numerically stable and computationally efficient in atmospheric boundary layers over California. The computational advantage originates from sparse-matrix techniques and the low frequency for communicating feedbacks between CVPS and other local operators. Based on surface Ox (O3 + NO2) simulations in the Southern California Air Quality Study domain ( Harley et al., 1993 ; Jacobson et al., 1996 ) on a dual quad-core Linux processor, the ratio of simulation/computer times may reach two for three-dimensional modeling using a classic horizontal advection solver and the CVPS operator.