A database and tool for boundary conditions for regional air quality modeling: description and evaluation

Transported air pollutants receive increasing attention as regulations tighten and global concentrations increase. The need to represent international transport in regional air quality assessments requires improved representation of boundary concentrations. Currently available observations are too sparse vertically to provide boundary information, particularly for ozone precursors, but global simulations can be used to generate spatially and temporally varying lateral boundary conditions (LBC). This study presents a public database of global simulations designed and evaluated for use as LBC for air quality models (AQMs). The database covers the contiguous United States (CONUS) for the years 2001–2010 and contains hourly varying concentrations of ozone, aerosols, and their precursors. The database is complemented by a tool for configuring the global results as inputs to regional scale models (e.g., Community Multiscale Air Quality or Comprehensive Air quality Model with extensions). This study also presents an example application based on the CONUS domain, which is evaluated against satellite retrieved ozone and carbon monoxide vertical profiles. The results show performance is largely within uncertainty estimates for ozone from the Ozone Monitoring Instrument and carbon monoxide from the Measurements Of Pollution In The Troposphere (MOPITT), but there were some notable biases compared with Tropospheric Emission Spectrometer (TES) ozone. Compared with TES, our ozone predictions are high-biased in the upper troposphere, particularly in the south during January. This publication documents the global simulation database, the tool for conversion to LBC, and the evaluation of concentrations on the boundaries. This documentation is intended to support applications that require representation of long-range transport of air pollutants

Development of the high-order decoupled direct method in three dimensions for particulate matter: Enabling advanced sensitivity analysis in air quality models

The high-order decoupled direct method in three dimensions for particulate matter (HDDM-3D/PM) has been implemented in the Community Multiscale Air Quality (CMAQ) model to enable advanced sensitivity analysis. The major effort of this work is to develop high-order DDM sensitivity analysis of ISORROPIA, the inorganic aerosol module of CMAQ. A case-specific approach has been applied, and the sensitivities of activity coefficients and water content are explicitly computed. Stand-alone tests are performed for ISORROPIA by comparing the sensitivities (first- and second-order) computed by HDDM and the brute force (BF) approximations. Similar comparison has also been carried out for CMAQ sensitivities simulated using a week-long winter episode for a continental US domain. Second-order sensitivities of aerosol species (e.g., sulfate, nitrate, and ammonium) with respect to domain-wide SO2, NOx, and NH3 emissions show agreement with BF results, yet exhibit less noise in locations where BF results are demonstrably inaccurate. Second-order sensitivity analysis elucidates poorly understood nonlinear responses of secondary inorganic aerosols to their precursors and competing species. Adding second-order sensitivity terms to the Taylor series projection of the nitrate concentrations with a 50% reduction in domain-wide NOx or SO2 emissions rates improves the prediction with statistical significance. © Author(s) 2012

Photochemical grid model implementation and application of VOC, NO_x, and O₃ source apportionment

For the purposes of developing optimal emissions control strategies, efficient approaches are needed to identify the major sources or groups of sources that contribute to elevated ozone (O3) concentrations. Source-based apportionment techniques implemented in photochemical grid models track sources through the physical and chemical processes important to the formation and transport of air pollutants. Photochemical model source apportionment has been used to track source impacts of specific sources, groups of sources (sectors), sources in specific geographic areas, and stratospheric and lateral boundary inflow on O3. The implementation and application of a source apportionment technique for O3 and its precursors, nitrogen oxides (NOx) and volatile organic compounds (VOCs), for the Community Multiscale Air Quality (CMAQ) model are described here. The Integrated Source Apportionment Method (ISAM) O3 approach is a hybrid of source apportionment and source sensitivity in that O3 production is attributed to precursor sources based on O3 formation regime (e.g., for a NOx-sensitive regime, O3 is apportioned to participating NOx emissions). This implementation is illustrated by tracking multiple emissions source sectors and lateral boundary inflow. NOx, VOC, and O3 attribution to tracked sectors in the application are consistent with spatial and temporal patterns of precursor emissions. The O3 ISAM implementation is further evaluated through comparisons of apportioned ambient concentrations and deposition amounts with those derived from brute force zero-out scenarios, with correlation coefficients ranging between 0.58 and 0.99 depending on specific combination of target species and tracked precursor emissions. Low correlation coefficients occur for chemical regimes that have strong nonlinearity in O3 sensitivity, which demonstrates different functionalities between source apportionment and zero-out approaches, where appropriate use depends on whether source attribution or source sensitivity is desired

A method for evaluating spatially-resolved NO_x emissions using Kalman filter inversion, direct sensitivities, and space-based NO₂ observations

International audienceAn inverse modeling method was developed and tested for identifying possible biases in emission inventories using satellite observations. The relationships between emission inputs and modeled ambient concentrations were estimated using sensitivities calculated with the decoupled direct method in three dimensions (DDM-3D) implemented within the framework of the Community Multiscale Air Quality (CMAQ) regional model. As a case study to test the approach, the method was applied to regional ground-level NOx emissions in the southeastern United States as constrained by the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) satellite derived observations of NO2 column densities. A controlled "pseudodata" scenario with a known solution was used to establish that the methodology can achieve the correct solution, and the approach was then applied to a summer 2004 period where the satellite data are available. The results indicate that emissions biases differ in urban and rural areas of the southeast. The method suggested slight downward (less than 10%) adjustment to urban emissions, while rural region results were found to be highly sensitive to NOx processes in the upper troposphere. As such, the bias in the rural areas is likely not solely due to biases in the ground-level emissions. It was found that CMAQ was unable to predict the significant level of NO2 in the upper troposphere that was observed during the NASA Intercontinental Chemical Transport Experiment (INTEX) measurement campaign. The reasons for the underestimation likely include combination of a lack of lightning emissions and a short modeled lifetime of NOx aloft. Therefore, the best correlation between satellite observations and modeled NO2 column densities, as well as comparison to ground-level observations of NO2, was obtained from performing the inverse while accounting for the significant presence of NO2 in the upper troposphere not captured by the regional model

Sensitivity of Ambient Atmospheric Formaldehyde and Ozone to Precursor Species and Source Types Across the United States

Formaldehyde (HCHO) is an important air pollutant from both an atmospheric chemistry and human health standpoint. This study uses an instrumented photochemical Air Quality Model, CMAQ-DDM, to identify the sensitivity of HCHO concentrations across the United States (U.S.) to major source types and hydrocarbon speciation. In July, biogenic sources of hydrocarbons contribute the most (92% of total hydrocarbon sensitivity), split between isoprene and other alkenes. Among anthropogenic sources, mobile sources of hydrocarbons and nitrogen oxides (NO_<i>x</i>) dominate. In January, HCHO is more sensitive to anthropogenic hydrocarbons than biogenic sources, especially mobile sources and residential wood combustion (36% of national hydrocarbon sensitivity). While ozone (O₃) is three times more sensitive to NO_<i>x</i> than hydrocarbons across most areas of the U.S., HCHO is six times more sensitive to hydrocarbons than NO_<i>x</i>, largely due to sensitivity to biogenic precursors and the importance of low-NO_<i>x</i> chemistry. In winter, both HCHO and O₃ show negative sensitivity to NO_<i>x</i> (increases with the removal of NO_<i>x</i>), although O₃ increases are larger. Relative sensitivities do not change substantially across different regions of the country

Evaluation of dust and trace metal estimates from the Community Multiscale Air Quality (CMAQ) model version 5.0

The Community Multiscale Air Quality (CMAQ) model is a state-of-the-science air quality model that simulates the emission, transformation, transport, and fate of the many different air pollutant species that comprise particulate matter (PM), including dust (or soil). The CMAQ model version 5.0 (CMAQv5.0) has several enhancements over the previous version of the model for estimating the emission and transport of dust, including the ability to track the specific elemental constituents of dust and have the model-derived concentrations of those elements participate in chemistry. The latest version of the model also includes a parameterization to estimate emissions of dust due to wind action. The CMAQv5.0 modeling system was used to simulate the entire year 2006 for the continental United States, and the model estimates were evaluated against daily surface-based measurements from several air quality networks. The CMAQ modeling system overall did well replicating the observed soil concentrations in the western United States (mean bias generally around ±0.5 μg m&minus;3); however, the model consistently overestimated the observed soil concentrations in the eastern United States (mean bias generally between 0.5–1.5 μg m&minus;3), regardless of season. The performance of the individual trace metals was highly dependent on the network, species, and season, with relatively small biases for Fe, Al, Si, and Ti throughout the year at the Interagency Monitoring of Protected Visual Environments (IMPROVE) sites, while Ca, K, and Mn were overestimated and Mg underestimated. For the urban Chemical Speciation Network (CSN) sites, Fe, Mg, and Mn, while overestimated, had comparatively better performance throughout the year than the other trace metals, which were consistently overestimated, including very large overestimations of Al (380%), Ti (370%) and Si (470%) in the fall. An underestimation of nighttime mixing in the urban areas appears to contribute to the overestimation of trace metals. Removing the anthropogenic fugitive dust (AFD) emissions and the effects of wind-blown dust (WBD) lowered the model soil concentrations. However, even with both AFD emissions and WBD effects removed, soil concentrations were still often overestimated, suggesting that there are other sources of errors in the modeling system that contribute to the overestimation of soil components. Efforts are underway to improve both the nighttime mixing in urban areas and the spatial and temporal distribution of dust-related emission sources in the emissions inventory