Atmospheric and Climate Sciences
Vol. 2  No. 3 (2012) , Article ID: 21376 , 36 pages DOI:10.4236/acs.2012.23025

Application, Evaluation, and Process Analysis of the US EPA’s 2002 Multiple-Pollutant Air Quality Modeling Platform

Kai Wang, Yang Zhang*

Department of Marine, Earth, and Atmospheric Sciences, North Carolina State University, Raleigh, USA

Email: *yzhang9@ncsu.edu

Received May 7, 2012; revised June 1, 2012; accepted June 9, 2012

Keywords: Multi-Pollutant; Air Toxics; Model Evaluation; Process Analysis

ABSTRACT

A multiple-pollutant version of CMAQ v4.6 (i.e., CMAQ-MP) has been applied by the US EPA over continental US in 2002 to demonstrate the model’s capability in reproducing the long-term trends of ambient criteria and hazardous air pollutants (CAPs and HAPs, respectively) in support of regulatory analysis for air quality management. In this study, a comprehensive model performance evaluation for the full year of 2002 is performed for the first time for CMAQ-MP using the surface networks and satellite measurements. CMAQ-MP shows a comparable and improved performance for most CAPs species as compared to an older version of CMAQ that did not treat HAPs and used older versions of national emission inventories. CMAQ-MP generally gives better performance for CAPs than for HAPs. Max 8-h ozone (O3) mixing ratios are well reproduced in the O3 season. The seasonal-mean performance is fairly good for fine particulate matter (PM2.5), sulfate, and mercury (Hg) wet deposition and worse for other CAPs and HAPs species. The reasons for the model biases may be attributed to uncertainties in emissions for some species (e.g., ammonia (NH3), elemental carbon (EC), primary organic aerosol (POA), HAPs), gas/aerosol chemistry treatments (e.g., secondary organic aerosol formation, meteorology (e.g., overestimate in summer precipitation), measurements (e.g.,), and the use of a coarse grid resolution. CMAQ cannot well reproduce spatial and seasonal variations of column variables except for nitrogen dioxide (NO2) and the ratio of column mass of HCHO/NO2. Possible reasons include inaccurate seasonal allocation or underestimation of emissions, inaccurate BCONs at higher altitudes, lack of model treatments such as mineral dust or plume-in-grid process, and limitations and errors in satellite data retrievals. The process analysis results show that in addition to transport, gas chemistry or aerosol/emissions play the most important roles for O3 or PM2.5, respectively. For most HAPs, emissions are important sources and cloud processes are a major sink. Simulated and HCHO/NO2 indicate VOC-limited chemistry in major urban areas throughout the year and in other non-urban areas in winter, but NOX-limited chemistry in most areas in summer.

1. Introduction

Hazardous air pollutants (HAPs) or air toxics are the pollutants known to cause serious effects on human health, such as cardiovascular, neurological, and other organ system problems and adverse environmental issues. 188 air toxics are identified and regulated under the 1990 Clean Air Act. HAPs are emitted from a variety of sources, including large manufacturing facilities, combustion facilities, small commercial, and both onroad and nonroad mobile sources [1]. In contrast with criteria air pollutants CAPs such as O3 and PM2.5, HAPs are normally controlled by state or local air toxics monitoring programs rather than the National Ambient Air Quality Standards (NAAQS) [2]. In recent years, the US Environmental Protection Agency (EPA) has launched several programs (e.g., National Air Toxics Assessment), in order to gain a better understanding of the impacts of air toxics emissions on public health and environment and eventually strengthen the nation’s air quality management system [3]. One of the major activities as part of those programs is the development and evaluation of the 2002 multiscale multiple pollutants (MP) air quality modeling platform to integrate across the complex chemical and physical processes for MPs in a single modeling framework in support of scientific research and regulatory analysis.

The US EPA’s Models-3 Community Multiscale Air Quality (CMAQ) modeling system was developed in order to support both air quality regulatory assessments by governmental agencies and scientific studies by research institutions [4]. CMAQ has been extensively applied over a wide range of meteorological conditions and geographical areas in order to address air quality issues related to CAPs such as ozone (O3) and fine particulate matter (PM2.5) during the past decades [5-15]. However, CMAQ only simulates CAPs, which hinders its application for HAPs. There is a growing awareness that CAPs and HAPs controls should be considered together because air quality issues in many areas of the US and abroad involve both types of pollutants [2]. The assessment of the model’s capability in representing HAPs together with CAPs is critical to the development of costeffective emission control strategies for both CAPs and HAPs. Accurate modeling of this complex MP system requires that a broad range of temporal and spatial scales of MP interactions be considered simultaneously. To address this issue and further advance the “one-atmosphere” modeling capability of CMAQ, an MP version of CMAQ (referred to as CMAQ-MP hereafter) has been developed by the US EPA to simulate O3, PM2.5, mercury (Hg), and other HAPs (or air toxics) in a single model framework.

Multiple full year simulations with CMAQ-MP hereafter have been conducted by the US EPA over domains that cover the entire US or a portion of continental US (CONUS) for 2002 at different horizontal grid resolutions [3]. In this work, a comprehensive model evaluation is performed by comparing simulated concentrations of O3, PM2.5 and its components, precursors of O3 and PM2.5, major air toxics, as well as Hg deposition with ground-based and satellite measurements. Likely reasons that influence prediction biases of major pollutants are identified. The seasonal photochemical characteristics are examined and the relative contributions of controlling processes to the formation and destruction of major CAPs and HAPs are quantified through process analysis (PA) tool imbedded in CMAQ to provide important information to the development of the effective emission control strategies. The objectives of this study are to examine the capability and performance of CMAQ-MP in reproducing temporal and spatial patterns of air pollutants, quantify the contributions of major atmospheric processes to these pollutants, guide further diagnostic evaluations for model improvement and further development, and build confidence in the utilization of CMAQMP to air quality regulatory and research communities. To our best knowledge, this is the first comprehensive performance evaluation and process analysis of CMAQMP that simulates both CAPs and HAPs. Previous modeling of HAPs focus on either one species (e.g., Hg [1618] or diesel PM [19]) using a version of CMAQ with Hg (i.e., CMAQ-Hg) based on the CB05CLHG gas-phase mechanism or a subset of HAPs species (e.g., some HAPs [20] or several trace metal HAPs [21]) using a version of CMAQ for HAPs modeling based on a different gasphase mechanism (i.e., SAPRC99TX3) from that used in CMAQ-HAPs (i.e., CB05CLTX) and that used in CMAQMP (CB05TXHG). CB05TXHG combines HAPs treatments in CB05CLTX with Hg treatments in CB05CLHG, providing a comprehensive treatment for all major HAPs.

2. Model Configurations, Observational Data, and Evaluation Protocols

2.1. Model System and Configurations

CMAQ-MP has been developed by the US EPA through modifying algorithms for gas-phase chemistry, aerosols, clouds, and emissions used in the previous Hg and HAPs versions of the CMAQ (i.e., CMAQ-Hg and CMAQHAPs [22,23]) and merging them into the default CMAQ v4.6. CMAQ-MP, which has almost the same air toxics treatments as in the newer version of CMAQ v4.7 and CMAQ v5.0 in this study, includes elemental Hg (Hg0), divalent gaseous Hg (Hg(II) or Hg2), particulate Hg (PHg), 31 additional gas-phase HAPs, 6 toxic metals, and diesel PM as well as CAPs in the base version of CMAQ (details about air toxic species can be found at http://www.cmaq-model.org/cmaqwiki/index.php?title=CMAQv4.7.1_Multipollutant_Model). The chemical reactions for chlorine, Hg, and HAPs were added with the Carbon Bond Mechanism 2005 (CB05 [24]) and implemented together into CMAQ. The gas-phase mechanism of CMAQ-MP consists of 219 reactions, which include 156 reactions from base CB05 mechanism, 21 reactions for chlorine chemistry, 38 reactions for gas-phase HAPs, and 4 reactions for Hg [23]. Those reactions for HAPs and Hg mainly involve the oxidations by radicals such as hydroxyl (OH) and nitrate (NO3) radicals. A modified version of aerosol module version 4 (AERO4) also contains the treatment of sea salt emissions. The vertical diffusion module associated with aerosol emissions is updated for CMAQ-MP aerosol simulations [13]. CMAQMP uses the dry deposition module adopted from CMAQHg. The aqueous-phase chemistry of Hg is largely based on CMAQ-Hg, which includes 7 aqueousphase kinetic and 6 equilibrium reactions. The aqueous-phase chemistry for other species such as SO2 is based on the Regional Acid Deposition Model (RADM).

In this study, CMAQ-MP is applied to three annual (2002) simulations conducted by the US EPA (US EPA, 2008) over a parent domain (CONUS) at a horizontal grid resolution of 36-km and two sub-domains (portions of the eastern US (EUS) and the western US (WUS)) at a finer grid resolution of 12-km, as shown in Figure 1. The vertical resolution for each domain includes 14 layers from the surface to approximately 100 hPa (at ~15 km) using a sigma-pressure coordinate system. The height of first model layer is ~38 m. The meteorological inputs for each domain are simulated separately by the US EPA using the 5th generation PSU/ NCAR mesoscale model (MM5) v3.6.3 for the 36-km CONUS domain and MM5 v3.7.2 for the 12-km EUS domain, and by the Western Regional Air Partnership (WRAP) using MM5 v3.6.2 for the 12-km WUS domain [25]. All the three MM5 simulations are conducted with the four dimensional data assimilation (FDDA) and use the Pleim-Xiu land surface model, Asymmetric Convective Model (ACM) planetary boundary layer (PBL) parameterization schemes, and the RRTM longwave and Dudhia shortwave radiation schemes. While the EPA simulations use the Reisner I scheme for microphysics and the KainFritsch II scheme for the subgrid or cumulus convection, the WRAP simulation uses the Reisner II scheme and the Betts-Miller scheme. The MM5 hourly meteorological outputs are converted to CMAQ compatible inputs with the Meteorology-Chemistry Interface Processor (MCIP) version 3.1. The emissions are generated with the Sparse Matrix Operator Kernel Emission system (SMOKE) version 2.3 based on the EPA’s 2002 National Emissions Inventory (NEI) v3.0 for all domains. The boundary conditions (BCONs) and initial conditions (ICONs) of the 36-km domain are provided by a global chemistry transport model, GEOS-Chem [3], for key CAPs and Hg species and those of the 12-km domains are taken from the 36-km simulation. For HAPs species, BCONs of the 36-km domain for formaldehyde (HCHO) and acetaldehyde (ALD2) are also from GEOS-Chem, but those for other species are static and based on scientific literatures and available field studies [1,26]. A ten-day spin-up period from 12/22 to 12/31 2001 is used to minimize the influence of the ICONs for each simulation.

2.2. Evaluation Protocols and Observational Data

Currently the model performance evaluation for most CAPs and related variables wet depositions has been guided by US EPA [27]. However, there are no recommended performance goals or objectives for evaluating HAPs. The recommended statistics for O3 or PM2.5 may not be appropriate for air toxics. Seigneur et al. [28] indicated that the model performance for HAPs may be relatively poor due to higher uncertainties in toxics emissions than in the emissions of CAPs. In this work, an

Figure 1. The CMAQ modeling domain. The black, red, and blue boxes denote domains over the 36-km continental US, the 12-km western US, and the 12-km eastern US, respectively (filled yellow, orange, blue, green, and red colors denote sub-regions northeast, southeast, Midwest, central and west for statistics).

operational model performance evaluation for O3, PM2.5 and its speciated components such as, , , EC, and OC, Hg wet deposition, and a selected set of HAPs is conducted using available routine surface monitoring data and satellite column data (Table 1). The surface data include those from the Clean Air Status and Trends Network (CASTNET), the Interagency Monitoring of Protected Visual Environments (IMPROVE), the Speciation Trends Network (STN), the Aerometric Information Retrieval System (AIRS)-Air Quality System (AQS), the Southeastern Aerosol Research and Characterization study (SEARCH), the National Acid Deposition Program (NADP), the Mercury Deposition Network (MDN), and the National Air Toxics Trends Stations (NATTS). Most of these networks are described in Eder and Yu [10] and Zhang et al. [6].

The satellite column data include the tropospheric CO columns from the Measurements of Pollution in the Troposphere (MOPITT) [29], the tropospheric NO2, HCHO columns, and their ratios (HCHO/NO2) from the Global Ozone Monitoring Experiment (GOME) [30], the tropospheric O3 residuals (TORs) from the Total Ozone Mapping Spectrometer/the Solar Backscattered Ultraviolet (TOMS/SBUV) [31], the AOD from the Moderate Resolution Imaging Spectroradiometer (MODIS) [32].

In addition to spatial plots, scatter plots, and time series plots, the model performance is examined using statistical metrics that follow Zhang et al. [6] including the mean bias (MB), correlation coefficient (R), the normalized mean bias (NMB), the normalized mean error (NME), and root mean square error (RMSE). The evaluation for surface predictions is conducted primarily using the EPA’s Atmospheric Model Evaluation Tool (AMET). AMET is a software package developed by EPA that can perform the operational evaluation of complex models. The column abundances of CO, NO2, HCHO, O3, and the ratios of column HCHO/NO2 are calculated using predicted concentrations from CMAQ and meteorologycal/ domain data (i.e., temperature, pressure, and layer thickness) from MM5 and converted into Dobson Unit (DU) for O3 and molecules·cm2 for other species for comparison with satellite data. AODs are estimion source regions (figures not shown), which provides the rationale for many studies that used GOME NO2 columns as the constraints for emission inventories of NOX [59]. The NO2 columns over industrial source regions are the lowest in the summer due to a rapid loss by the reaction of NO2 with OH. The high winter NO2 columns are likely resulted from a combined effect of a decreased loss of NO2 via its reaction with OH and slightly increased emissions as compared to the summer [30]. CMAQ are well correlated with the GOME measurements throughout the whole year with R values of 0.74 to 0.85. The larger discrepancies (see Table 4) in fall and winter can be attributed to several factors including possible overestimation of NOX emissions in those seasons and uncertainties in model inputs, treatments, and satellite measurements and retrievals. Boersma et al. [60] and some other studies [59,61] showed that different NO2 column retrieval approaches may lead to ±5 × 1014 - 1 × 1015 molecules∙cm−2 for additive error and ±35% - 60% for relative error over polluted areas, particularly in winter. It is also worth noting that unlike TORs, the tropospheric NO2 columns are insensitive to the tropopause definition because the contributions to NO2 columns from the upper troposphere and lower stratosphere are negligibly small as compared to those from lower troposphere, especially over polluted regions [61]. This may partly explain the better performance of this study, since CMAQ typically gives more accurate predictions at lower altitudes [15]. Despite a small domainwide bias in spring and summer, the model performance in terms of both magnitude and spatial distribution can be potentially improved with more accurate emissions and model treatments. For example, there might also be missing sources of NOX emissions such as lightning emissions, which could be important in spring and summer. Estimations from other studies [62] show that the resultant NO2 columns produced by lightning can go up to (0.5 - 2.0) × 1015 molecules∙cm–2 over the southern US, the Gulf of Mexico, and western North Atlantic in May. As discussed in Zhang et al. [8], the plume-in-grid treatment in CMAQ for large US power plants can result in improved column NO2 performance in eastern US in summer.

Figure 10 shows the observed and simulated seasonalmean tropospheric HCHO columns over the 36-km CONUS domain in 2002. Both GOME and CMAQ show strong seasonal variations of HCHO columns with values of about a factor of two higher in summer than in winter.

GOME measurements show high HCHO columns over the southeastern US, particularly in summer, which is well captured by CMAQ despite some overpredictions. The spatial and temporal variability of HCHO columns over the southeastern US in the model correlates clearly with biogenic and biomass burning emissions (figures not shown here) and is believed to be largely driven by oxidation of biogenic VOCs (BVOCs) (e.g., isoprene and terpene) [63]. As shown in Table 4, CMAQ overpredicts HCHO columns in all seasons except for winter. This discrepancy could be in part due to the relatively high yield of HCHO from isoprene and terpene in the CB05 chemical mechanism, particularly in warm seasons and uncertainties in the emission inventory, particularly for biogenic emissions. More importantly, according to Stavrakou et al. [63], the GOME HCHO columns retrieved by Belgian Institute for Space Aeronomy (BIRA)/ Royal Netherlands Meteorological Institute (KNMI) used in this study are about 4 × 1015 molecules∙cm–2 (by 30%) lower in summer over the eastern US and about 2 × 1015 molecules∙cm–2 higher in winter over the US than another set of GOME columns retrieved by Harvard University [64], which used trace gas profiles from GEOSChem model and a different approach to calculate air mass factor. This indicates that the uncertainties in satellite retrievals may also be a contributor to the discrepancy between CMAQ and satellite HCHO columns.

3.4.2. AOD

Figure 11 shows observed and simulated seasonal-mean AODs over the 36-km CONUS domain in 2002. Both MODIS and CMAQ AODs show consistent seasonal variations with the highest values in summer and the lowest in winter. They, however, display quite different spatial distributions over the CONUS domain with the most noticeable differences in the western US. There is a persistently high level of AODs that is up to 0.6 in summer and spring over the northwestern US, western US, and northern Mexico observed by MODIS in 2002. In contrast, CMAQ AODs are much lower (by a factor of 3 - 4) over those regions with only up to about 0.15 in summer. In addition, CMAQ did not reproduce elevated AODs (with values of up to 0.3) over Pacific and off the Pacific coast observed by MODIS in spring as the results of trans-Pacific transport of Asian air pollutants and dust storms, potentially due to the errors in lateral BCONs. CMAQ does predict the enhanced AODs in summer over the eastern US observed by MODIS although they are lower by a factor of two than MODIS. Statistically, CMAQ underpredicts AODs for all seasons with NMBs of –44.6% to –17.0%. These findings are consistent with those of Zhang et al. [8]. Several possible reasons may explain the discrepancies between MODIS and CMAQ AODs. First, the lack of model treatment of mineral dust

Figure 10. Spatial distributions of seasonal-mean tropospheric HCHO columns from GOME and CMAQ over CONUS in 2002. (a) GOME 36-km; (b) CMAQ 36-km.

Figure 11. Spatial distributions of seasonal-mean AODs from MODIS and CMAQ over CONUS in 2002. (a) MODIS 36-km; (b) CMAQ 36-km.

in CMAQ may lead to the underprediction of AODs over the arid areas of the western US. Second, the inaccurate predictions of PM2.5 concentrations, particularly the underprediction of and OC (as shown in Section 3.2) over the southeastern US can contribute significantly to the underestimate of AOD in the eastern US. Third, there are uncertainties in BCONs of PM2.5 and its components. Kaufman et al. [65] derived the background AODs to be 0.052 at 500 nm over the Pacific Ocean by using Aerosol Robotic network (AERONET) data. However, the averaged CMAQ AODs over the Pacific Ocean in this work are only from 0.015 to 0.039 in different seasons. This reflects that the BCONs for PM2.5 species might be too low from GEOS-Chem. Fourth, uncertainties exist in the empirical equations and the associated parameters for the AOD calculation. For example, the equations used in this study do not explicitly consider the contribution of. They also completely exclude the other fine-mode inorganic aerosols and coarse-mode aerosols (e.g., soils and sea salts). A set of modified empirical equations are being developed and will be applied in the future work to improve the model-derived AODs (Wang and Zhang, Implementation of dust emission and heterogeneous chemistry into the Community Multiscale Air Quality Model and an initial application to April 2001 Asian dust storm episode, manuscript in review). Finally, similar to other satellite data, there are limitations and uncertainties in the MODIS data used in this work. For example, according to Remer et al. [32], the uncertainty of MODIS monthly AODs (denoted as τ) can be up to ±0.05 ± 0.15τ over land because of clouds and surface reflectance. More recently, Drury et al. [66] found that there are some errors in the surface reflectance estimates in MODIS operational AOD products used in this study, which can lead to high biases of AODs especially over the western and central US. Their results by using improved AOD retrieval algorithm showed more consistent pattern as our CMAQ AODs in summer.

4. Process Analysis

Two process analysis approaches are embedded in CMAQ and they are integrated process rate (IPR) analysis and integrated reaction rate (IRR) analysis. IPRs assess the net effects of each atmospheric process simulated in CMAQ while IRRs calculate the rates of change of species concentration due to individual gas-phase reactions and track the chemical transformation pathways. Both IPRs and IRRs have been used to study various issues such as O3 chemistry and transport [9,67,68], regional and long range transport of air pollutants [9,15], and controlling processes/process budgets of different air pollutants [69,70]. In this section, the relative contributions of controlling processes to the formation and destruction of the selected CAPs and HAPs species are quantified through IPR analysis and the seasonal photochemical characteristics are examined through IRR analysis for January (representing winter) and July (representing summer) 2002.

4.1. IPR Analysis

The original outputs of IPRs are combined to represent several major processes including horizontal transport (sum of horizontal advection and diffusion), vertical transport (sum of vertical advection and diffusion), gasphase chemistry, aerosol processes (the net effect of gas-to-particle mass transfer and coagulation), emissions, dry deposition, and cloud processes (the net effect of cloud attenuation of photolytic rates, convective and nonconvective mixing and scavenging by clouds, aqueousphase chemistry, and wet deposition). The process contribution can be either positive or negative, indicating build-up or removal, respectively, of a species concentration due to a specific process.

Figure 12 depicts the process budgets for selected CAPs species including NOX, O3, , and PM2.5 in PBL over different sub-regions. The process budgets for NOX in both months show very similar variation with major contribution coming from emissions and major removal by chemistry. The contribution from transport seems to be higher in winter, indicating the higher wind speed in cold season. The emission rates for NOX are the highest over Midwest and the lowest over the western US in both months. The removal rate of NOX due to gas-phase chemistry is comparable between winter and summer, due to different reasons. In winter, the removal of NOX is mainly caused by the strong titration of O3, but in summer, NOX is mainly removed by radicals. In contrast, the processes contributing to O3 show a strong seasonality, with much higher formation of O3 from gasphase chemistry over all sub-regions in summer than in winter. In summer, the highest chemistry production over Midwest is consistent with the highest precursor emissions (e.g., NOX). The vertical transport and dry deposition are two major removal processes for O3 over all sub-regions. As expected, the contribution from chemistry is much weaker in winter. The horizontal/vertical transport instead plays more important role in the O3 budgets. The high values of O3 build-up from vertical transport and removal from horizontal transport over the western US indicate the persistent period of high pressure system locating over the western US in January 2002 that transports more O3 from the free troposphere to the PBL and horizontally out of western US. The opposite vertical transport for O3 over the western US in summer indicates the low pressure system and downward turbulent transport. For, the aerosol process is the

(a)(b)(c)(d)

Figure 12. The monthly-mean contributions of individual processes to the concentrations of selected criteria air pollutants: (a) NOX, (b) O3, (c), and (d) PM2.5 over different sub-regions of CONUS domain in January (left panel) and July (right panel) 2002. HORZ, VERT, DDEP, CLDS, CHEM, AERO, EMIS denote the processes of horizontal transport, vertical transport, dry deposition, cloud process, chemistry, aerosol chemistry, and emissions, respectively.

dominant contributor over most sub-regions in winter. While in summer the higher temperature prevents HNO3 from condensing onto the existing particle surface to form, although the HNO3 concentrations are higher. In particular, over the central and western US, aerosol process removes significant amount of via evaporation and desorption. Besides aerosol process, horizontal/vertical transport and cloud processes over most sub-regions in winter and vertical transport over the central/western US in summer also play important roles in the budget. The processes contributing to PM2.5 also show a strong seasonality. The overall emissions are comparable between two months with higher emission contributions over northeastern, southeastern, and Midwest US in winter and higher emission contributions over central and western US in summer. The removal of PM2.5 due to dry deposition is higher in summer than winter due to the general higher dry deposition velocity of aerosols over more vegetated areas. The changes of PM2.5 due to other processes are complicated over different subregions in both months. For example, the aerosol process tends to remove PM2.5 over the northeastern US and southeastern US, where ocean grid cells are included in the IPR calculation in winter because of a negative contribution to aerosol process of particulate-phase chloride (figure not shown) due to the fact that the reaction NaCl(s) + HNO3(g) ® NaNO3(s) + HCl(g) is favorable in winter. The negative budget of PM2.5 due to aerosol processes over the western US in summer is mainly due to the loss of and SOA (figures not shown), both of which have relatively low precursor emissions and high removal rates due to gas-particle equilibrium favoring their volatility to the gas phase over that region. Similar to most of other species, horizontal/vertical transport are also important for PM2.5.

Figure 13 depicts the process budgets for selected HAPs species Hg(II), PHg, HCHO, and particulate lead in PBL over different sub-regions. The gas-phase chemistry, emission, and horizontal/vertical transport (except horizontal transport in the Midwest and western US and vertical transport in the central US) dominate the production of Hg(II) and dry deposition and cloud processes dominate the removal of Hg(II) over most sub-regions in both months, but the magnitude of IPR for each process has a strong seasonality. For example, the IPRs of chemistry are much higher in summer because of higher oxidant levels. The IPR of dry deposition is comparable to that of cloud processes in both months, indicating that the wet deposition may also contribute significantly to the removal of Hg(II). The signs of IPRs for horizontal/vertical transports are more diverse in winter than summer, indicating a much different wind field pattern in some regions in winter. The IPRs of emissions for PHg also indicate that the major sources of Hg are located in the northeastern and Midwest US. The IPRs for PHg also show a strong seasonality, especially for aerosol and cloud processes and to a lesser extent for horizontal and vertical transport. The aerosol process contributes to the formation of PHg and the cloud process contributes to the removal. The contributions from both processes are much higher in summer due to higher concentrations of oxidants, which lead to higher aqueousand particulate-phase oxidation of Hg. To a lesser extent, the remaining processes also play some roles in the PHg budgets. The IPRs for HCHO show a strong seasonality with much higher contributions in summer than winter. Both emission and chemistry contribute to the formation of HCHO. The IPRs for chemistry, however, are about 5 to 10 times higher over different sub-regions in summer than winter, resulting from much higher direct and precursor emissions and rates of formation from precursors due to a stronger oxidation capability. The vertical transport, dry deposition, and cloud processes are the major processes to remove HCHO from the atmosphere. Unlike other HAPs, the seasonality for particulate lead is not evident. Emission is the major or only source for the build-up of lead over almost all sub-regions, indicating that the uncertainties in emission inventory may contribute significantly to model biases as discussed in the previous section (see Table 3). Cloud processes act as a major removal process for particulate lead followed by horizontal transport, vertical transport, and dry deposition. The contribution from aerosol processes is zero due to the assumption of chemical inertia of lead in CMAQ. The vertical transport for particulate lead and PHg plays a different role, indicating that the long-range transport of PHg is more important than particulate lead.

4.2. IRR Analysis

CB05 used in this study include 219 reactions. The IRRs of those reactions are grouped into 43 products according to the reactions for radical initiation, propagation, production, and termination (see Table 1 from Zhang et al. [9] for most products). Figure 14 shows the monthlymean spatial distributions of photochemical indicators of surface layer and column HCHO/NO2 predicted by CMAQ and column HCHO/NO2 observed by GOME satellite. As reported by Zhang et al. (2009b), several photochemical indicators have been proposed in the past in order to determine the NOX- or VOC-limited O3 chemistry in the regional modeling studies [71,72]. The ratio between the production rates of H2O2 and HNO3 () has been widely used in chemical indicator analysis due to its robust theoretical background [71]. less than 0.2 typically indicates a VOC-limited O3 chemistry and a larger value indicates a NOX-limited O3 chemistry [9]. As shown in Figure 14(a), during winter, most regions over US except for some areas over the western US have VOC-

(a)(b)(c)(d)

Figure 13. The monthly-mean contributions of individual processes to the concentrations of selected hazardous air pollutants: (a) Hg(II); (b) PHg; (c) HCHO; and (d) Particulate lead over different sub-regions of CONUS domain in January (left panel) and July (right panel) 2002. HORZ, VERT, DDEP, CLDS, CHEM, AERO, EMIS denote the processes of horizontal transport, vertical transport, dry deposition, cloud process, chemistry, aerosol chemistry, and emissions, respectively.

(a)(b)(c)

Figure 14. The monthly-mean spatial distributions of photochemical indicators of (a) surface layer and (b) column HCHO/NO2 predicted by CMAQ and (c) column HCHO/NO2 observed by GOME satellite in January (left panel) and July (right panel) 2002. Blank in GOME observations represents no data available.

limited O3 chemistry due to high NOX and low BVOC emissions. By contrast, while the major cities and industry areas remain VOC-limited chemistry, all other areas (mostly rural and remote areas) change to NOX-limited O3 chemistry in summer. The results shown here are overall consistent with those reported by Zhang et al. [9] and Liu et al. [70]. In order to verify the robustness of as a photochemical indicator, we also calculate the column ratio of HCHO/NO2, another indicator recommended by Martin et al. [72]. The rationale to use two column species to indicate the surface photochemistry is due to that the bulk of their columns are within the lower mixed layer over polluted regions and the columns are closely related to VOC and NOX emissions [72,73]. Another reason is that there are space-based observations of both tropospheric HCHO and NO2 column mass and the modeled ratio of HCHO/NO2 can be further examined by largescale and long term satellite observations. The transition value for column HCHO/NO2 originally used by Martin et al. [72] was 1, but Duncan et al. [73] suggested values of 1.2 - 2.2, above which O3 chemistry is VOC-limited. As shown in Figures 14(a)-(b), the spatial pattern of VOCvs. NOX-limited areas indicated by column HCHO/NO2 predicted by CMAQ is very similar to that of in both months, if a transition value of 1.6 is used for column HCHO/NO2. Comparing with satellite observations (Figure 14(c)), CMAQ demonstrates a promising accuracy in reproducing the spatial variation of column HCHO/NO2 in most areas, despite some discrepancies in some areas (e.g., in Texas and northern Mexico in January and in the Ohio valley in July), which can be attributed to the uncertainties in both model predicttions and satellite measurements. The above findings indicate that both and column HCHO/NO2 are robust indicators for development and assessment of various precursor emission reduction strategies for O3 control.

5. Summary and Conclusions

This study presents a comprehensive evaluation and analysis of several full year simulations over contiguous US domains using the US EPA’s the multiple-pollut-ant version of CMAQ v4.6 (i.e., the 2002 MP modeling platform). Model evaluation is performed by comparing simulated concentrations of O3, PM2.5, and its components, precursors O3 and PM2.5, and major air toxics as well as the Hg deposition with the measurements collected from ground-based monitoring networks and satellites. Our results show that CMAQ simulates well the spatial and seasonal variation of O3, especially during the O3 season and gives the best agreement with observed O3 mixing ratio range of 40 - 60 ppb. These results demonstrate a moderate to great improvement in O3 predictions compared to the previous studies for several reasons including the newest CB05 gas-phase chemistry mechanism with chloride related reactions, a new PBL scheme ACM2, and new emission inventories. Model performance for PM2.5 and its components is satisfactory or marginally-satisfactory. CMAQ predicts the best among all PM2.5 components, with a slight improvement compared with previous study which is likely attributed to updates in both convective cloud module and aerosol dry deposition module in CMAQ. The uncertainty associated with NH3 emissions is found to be indicative of the main reason for the model bias of. The performance for remains poor, despite some improvements in terms of statistics as compared to earlier studies. OC underpredictions are much worse than those of EC, particularly in summer, because of underpredictions of photochemically-produced SOA, in addition to uncertainties in the emissions of POA and SOA precursors in summer. CMAQ shows a satisfactory performance in predicting PM2.5 that is comparable to or even better than previous studies due to several model updates, although it overpredicts PM2.5 in winter mainly due to overpredictions in concentrations of other unknown PM2.5, and underpredicts it in summer mainly due to underpredictions in OC concentrations.

The overall model performance for HAPs is worse than CAPs due to several reasons. For example, the emission inventory for HAPs is not as accurate as that of CAPs, the model treatments for HAPs species are not as mature as those for CAPS, and there is a lack of routine measurements of HAPs. However, CMAQ does reasonably well in simulating seasonal Hg wet deposition, with consistent or even better performance as compared with previous studies because of several model updates. The model performance is slightly better in spring and fall than in summer and winter. The evaluation results for selected air toxics show a systematic underprediction for most species except for ALD2 throughout the year due to several reasons, including the incapability of the coarse grid resolution in resolving the high-level plume event, the underestimation of emissions for most of HAPs, and the simplified assumption of HAPs chemistry in current CMAQ-MP. The overall model performance in the 2002 MP modeling platform is fairly good for HCHO and ALD2, moderately good for benzene and particulate lead, and very poor for 1,3-butadiene and acrolein.

The spatial distribution and seasonal variations of GOME NO2 columns are generally well reproduced by CMAQ, with a good correlation throughout the year. Despite moderate underpredictions, CMAQ reasonably captures high MOPITT CO columns over source regions. Although relatively small NMBs for simulated TORs, CMAQ fails to capture the observed seasonal variations, likely due to uncertainties in the upper BCONs for O3 used in CMAQ. Moderate-to-significant overpredictions of HCHO columns from CMAQ occur in all seasons except for winter. CMAQ underpredicts MODIS AODs and fails to capture spatial distributions for all seasons. Several possible reasons for model biases in column predictions are identified. These include inaccurate seasonal allocation, underestimation of emissions, inaccurate BCONs in higher altitudes, lack of model treatments such as mineral dust or plume-in-grid process, as well as limitations and errors in satellite data retrievals.

The IPRs of the process analysis show that emissions are important sources for NOX, PM2.5, and many of HAPs such as Hg(II), PHg, and particulate lead over almost all the sub-regions in both seasons. Gas-phase chemistry is the dominant contributor to both HCHO and O3 especially in summer, however, it removes NOX significantly in both seasons. Aerosol processes contribute significantly to PHg formation and also play important but complex roles in the formation/removals of and PM2.5. Cloud processes remove most of HAPs significantly over all the sub-regions. The role of dry deposition is relatively more important for O3, HCHO, and Hg(II) especially in summer. Horizontal and vertical transport play important role for most of species, indicating the importance of accurate prediction of wind fields on air pollutants. The IPR results suggest that improving model treatments of those dominant processes may help to improve the model performance. The IRRs show a dominant NOX-limited chemistry in most areas but VOC-limited chemistry over urban and industry areas in summer and VOC-limited chemistry in winter over most of US, consistent with previous modeling studies and GOME satellite observations. The results indicate that integrated NOX/VOC emission controls should be considered over different regions in different seasons.

As illustrated in this study, the predictions of CAPs and HAPs from the 2002 MP 36-km and 12-km simulations are within the range or better than those reported in several recent EPA applications. This attests its scientific capability in assessing O3 and PM2.5 as well as air toxics for the purposes of the NAAQS Final Rule. The model evaluation also identifies several key areas for potential model improvements, thus providing guidance for sensitivity studies and further model development and improvement efforts and directions in the future.

6. Acknowledgements

This work was supported by the US EPA’s ICAP project, the National Research Initiative Competitive Grant No. 2008-35112-18758 from the USDA Cooperative State Research, Education, and Extension Service Air Quality Program, and the US EPA’s Science to Achieve Results (STAR) grant #R833863. Thanks are due to Sharon Phillips and Carey Jang, the US EPA, for some technical guidance and helpful discussions; Wyat Appel, the US EPA, for providing help on AMET analysis; Jack Fishman and his colleagues, NASA Langley Research Center, US, for providing the TOMS/SBUV and MOPITT CO satellite data; NASA MODIS Adaptive Processing System, for providing MODIS AOD data; TEMIS of the European Space Agency for providing GOME NO2 and HCHO data. Thanks are also due to George Pouliot of the US EPA for helpful discussions on uncertainties in emissions.

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NOTES

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