Plume injection height influences plume transport characteristics, such as range and potential for dilution. We evaluated plume injection height from a predictive wildland fire smoke transport model over the contiguous United States (U. S.) from 2006 to 2008 using satellite-derived information, including plume top heights from the Multi-angle Imaging SpectroRadiometer (MISR) Plume Height Climatology Project and aerosol vertical profiles from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). While significant geographic variability was found in the comparison between modeled plumes and satellite-detected plumes, modeled plume heights were lower overall. In the eastern U. S., satellite-detected and modeled plume heights were similar (median height 671 and 660 m respectively). Both satellite-derived and modeled plume injection heights were higher in the western U. S. (2345 and 1172 m, respectively). Comparisons of modeled plume injection height to satellite-derived plume height at the fire location (R-2 = 0.1) were generally worse than comparisons done downwind of the fire (R-2 = 0.22). This suggests that the exact injection height is not as important as placement of the plume in the correct transport layer for transport modeling.

We evaluated predictions of hourly PM2.5surface concentrations produced by the experimental BlueSky Gateway air quality modeling system during two wildfire episodes in southern California (Case 1) and northern California (Case 2). In southern California, the prediction performance was dominated by the prevailing synoptic weather patterns, which differentiated the smoke plumes into two types: narrow and highly concentrated during an offshore flow, and diluted and well‐mixed during a light onshore flow. For the northern California fires, the prediction performance was dominated by terrain and the limitations of predicting concentrations in a narrow valley, rather than by the synoptic pattern, which did not differ much throughout the wildfire episode. There was an over‐prediction bias for the maximum values during this episode. When the predicted values were compared to observed values, the best performance results were for the onshore flow during the southern California fires, indicating that the coarse grid used by BlueSky Gateway appropriately represented these well‐mixed conditions. Overall, the southern California fire predictions were biased low and the model did not reproduce the high hourly concentrations (>240μg/m3) observed by the monitors. The predicted results performed well against the observations for the northern California fires, with a large number of predicted values within acceptable range of the observed values.

ABSTRACT The main objective of this study was to investigate the capabilities of the receptor-oriented inverse mode Lagrangian Stochastic Particle Dispersion Model (LSPDM) with the 12-km resolution Mesoscale Model 5 (MM5) wind field input for the assessment of source identification from seven regions impacting two receptors located in the eastern United States. The LSPDM analysis was compared with a standard version of the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) single-particle backward-trajectory analysis using inputs from MM5 and the Eta Data Assimilation System (EDAS) with horizontal grid resolutions of 12 and 80 km, respectively. The analysis included four 7-day summertime events in 2002; residence times in the modeling domain were computed from the inverse LSPDM runs and HYPSLIT-simulated backward trajectories started from receptor-source heights of 100, 500, 1000, 1500, and 3000 m. Statistics were derived using normalized values of LSPDM- and HYSPLIT-predicted residence times versus Community Multiscale Air Quality model-predicted sulfate concentrations used as baseline information. From 40 cases considered, the LSPDM identified first- and second-ranked emission region influences in 37 cases, whereas HYSPLIT-MM5 (HYSPLIT-EDAS) identified the sources in 21 (16) cases. The LSPDM produced a higher overall correlation coefficient (0.89) compared with HYSPLIT (0.55–0.62). The improvement of using the LSPDM is also seen in the overall normalized root mean square error values of 0.17 for LSPDM compared with 0.30–0.32 for HYSPLIT. The HYSPLIT backward trajectories generally tend to underestimate near-receptor sources because of a lack of stochastic dispersion of the backward trajectories and to overestimate distant sources because of a lack of treatment of dispersion. Additionally, the HYSPLIT backward trajectories showed a lack of consistency in the results obtained from different single vertical levels for starting the backward trajectories. To alleviate problems due to selection of a backward-trajectory starting level within a large complex set of 3-dimensional winds, turbulence, and dispersion, results were averaged from all heights, which yielded uniform improvement against all individual cases. IMPLICATIONS Backward-trajectory analysis is one of the standard procedures for determining the spatial locations of possible emission sources affecting given receptors, and it is frequently used to enhance receptor modeling results. This analysis simplifies some of the relevant processes such as pollutant dispersion, and additional methods have been used to improve receptor-source relationships. A methodology of inverse Lagrangian stochastic particle dispersion modeling was used in this study to complement and improve standard backward-trajectory analysis. The results show that inverse dispersion modeling can identify regional sources of haze in national parks and other regions of interest.

The Central California Ozone Study (CCOS) was a multi-year program of meteorological and air quality monitoring, emission inventory development, data analysis, and air quality simulation modeling. Photochemical modeling studies were previously carried out using both the Comprehensive Air Quality Model with Extensions (CAMx) and the Community Multiscale Air Quality (CMAQ) model. Performance evaluations of the photochemical modeling have shown that the models tend to underpredict peak ozone concentrations, both at the surface and aloft, and that model performance did not meet traditional goals for ozone. The objective of this study was to understand and improve model performance for ozone and ozone precursors aloft. Data analysis of surface and aloft air quality and meteorological data was used to characterize the episodes studied. Model-to-measurement comparisons of air quality, meteorological, and integrated (e.g., pollutant flux) values, and sensitivity analyses were used to investigate model performance. These analyses suggested that emission estimates, particularly for wildfires, and regional transport and recirculation of ozone aloft through model's boundaries may account for a significant portion of the underprediction of ozone concentrations.

Smoke from biomass burning events, both large and small, contributes to air quality problems associated with elevated concentrations of particulate matter, ozone, and air toxics. Currently, real-time smoke predictions are via the Blue Sky smoke modeling framework. Blue Sky modularly links computer models of fuel consumption and emissions, fire, weather, and smoke dispersion into a system for predicting the cumulative impacts of smoke from prescribed fires, wild-fires, and agricultural fires. The Blue Sky smoke modeling framework has recently been upgraded in several key ways: satellite data are now incorporated, using the SMARTFIRE system, to provide information on the location and size of fires; the most recent fuel loading, fuel consumption, and emission models have been added; and the Community Multiscale Air Quality model (CMAQ) is being used to predict concentration fields of particulate matter and ozone nationally from both fire and anthropogenic emissions. A general overview of the Blue Sky program and a description of its current features are provided. In particular, we focus on Blue Sky products of interest to the air quality community, such as daily experimental predictions and how the products are being distributed through the BlueSky Gateway web portal.

To elucidate the relationship between factors resolved by the positive matrix factorization (PMF) receptor model and actual emission sources and to refine the PMF modeling strategy, speciated PM2.5 (particulate matter with aerodynamic diameter < 2.5 microm) data generated from a state-of-the-art chemical transport model for two rural sites in the eastern United States are subjected to PMF analysis. In addition to chi2 and R2 used to infer the quality of fitting, the interpretability of PMF factors with respect to known primary and secondary sources is evaluated using a root mean square difference analysis. For the most part, factors are found to represent imperfect combinations of sources, and the optimal number of factors should be just adequate to explain the input data (e.g., R2 > 0.95). Retaining more factors in the model does not help resolve minor sources, unless temporal resolution of the data is increased, thus allowing more information to be used by the model. If guided with a priori knowledge of source markers and/or special events, rotation of factors leads to more interpretable PMF factors. The choice of uncertainty weighting coefficients greatly influences the PMF modeling results, but it cannot usually be determined for simulated or real-world data. A simple test is recommended to check whether the weighting coefficients are suitable. However, uncertainties in the data divert PMF solutions even when the optimal weighting coefficients and number of factors are in place.

Fine particulate matter is believed to be more toxic than coarse particles and to exacerbate health problems such as respiratory and cardiopulmonary diseases. Specific organic compounds within atmospheric fine particulate material can be used to differentiate specific inputs from various emissions and thus is helpful in identifying the major urban air pollution sources that contribute to these health problems. Particular marker compounds that carry signature information about different emission sources (i.e., gasoline or diesel motor vehicles, wood smoke, meat cooking, vegetative detritus, and cigarette smoke) are reviewed. Aerosol organic types (e.g., from mass spectrometry data, which can also help in elucidation of carbonaceous material sources) are also discussed. Apportionment of the primary source contributions and atmospheric processes contributing to fine particulate matter and fine particulate organic material concentrations are outlined. This review provides an overview of the latest developments in chemical characterization approaches for identification and quantification of compounds in complex organic mixtures associated with fine atmospheric particles and their use in chemical mass balance (CMB) and positive matrix factorization (PMF) source apportionment models.

During the past decade, a proliferation of data, software systems, and analysis tools have emerged in various modeling communities. The heterogeneity of available data, data formats, software systems, and ad-hoc tools has fractured the awareness, access, and distribution of data and software tools. Consequently, analysts and decision makers are left with an assortment of analysis and modeling methods, as well as unconnected software systems in various stages of development. In response to these issues, the Joint Fire Science Program (JFSP), acting in concert with the interagency Fuels Management Committee, initiated the Software Tools and Systems (STS) Study in 2007 to address the proliferation of unconnected and unmanaged modeling systems in the fire and fuels domain. A strategic assessment was performed (Palmquist, 2008) that led directly to development of a conceptual design and a software design for a service-oriented, framework architecture for fuels treatment planning (Funk et al., 2009). Under the guidance of an interagency team, these designs were developed into the Interagency Fuels Treatment Decision Support System (IFT-DSS). In 2009, JFSP funded development of a proof of-concept version of the IFT-DSS (Funk, 2010). A fully functional version of the IFT-DSS is now under development.

Smoke from wildland fire is a growing concern as air quality regulations tighten and public acceptance declines. Wildland fire emissions inventories are important not only for understanding air quality impacts from smoke but also in quantifying sources of greenhouse gas emissions. Calculation of wildland fire emissions can be done using a number of models and methods. Under the Smoke and Emissions Model Intercomparison Project, comparisons between different methodologies are being analyzed by examining model-to-model variability. In addition, the relative importance of uncertainties in fire size information, available fuels information, consumption modeling techniques, and emissions factors are being compared. This work highlights the need for accurate fire information that integrates information from multiple datasets. We present a new effort that upgrades the SMARTFIRE-BlueSky Framework, providing constraints on fire information and other errors in the modeling chain, and resulting in an improved wildland fire emissions inventory.

The ability of receptor models to estimate regional contributions to fine particulate matter (PM2.5) was assessed with synthetic, speciated datasets at Brigantine National Wildlife Refuge (BRIG) in New Jersey and Great Smoky Mountains National Park (GRSM) in Tennessee. Synthetic PM2.5 chemical concentrations were generated for the summer of 2002 using the Community Multiscale Air Quality (CMAQ) model and chemically speciated PM2.5 source profiles from the U.S. Environmental Protection Agency (EPA)'s SPECIATE and Desert Research Institute's source profile databases. CMAQ estimated the "true" contributions of seven regions in the eastern United States to chemical species concentrations and individual source contributions to primary PM2.5 at both sites. A seven-factor solution by the positive matrix factorization (PMF) receptor model explained approximately 99% of the variability in the data at both sites. At BRIG, PMF captured the first four major contributing sources (including a secondary sul-fate factor), although diesel and gasoline vehicle contributions were not separated. However, at GRSM, the resolved factors did not correspond well to major PM2.5 sources. There were no correlations between PMF factors and regional contributions to sulfate at either site. Unmix produced five- and seven-factor solutions, including a secondary sulfate factor, at both sites. Some PMF factors were combined or missing in the Unmix factors. The trajectory mass balance regression (TMBR) model apportioned sulfate concentrations to the seven source regions using Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) trajectories based on Meteorological Model Version 5 (MM5) and Eta Data Simulation System (EDAS) meteorological input. The largest estimated sulfate contributions at both sites were from the local regions; this agreed qualitatively with the true regional apportionments. Estimated regional contributions depended on the starting elevation of the trajectories and on the meteorological input data.

Data analysis and modeling were performed to characterize the spatial and temporal variability of wintertime transport and dispersion processes and the impact of these processes on particulate matter (PM) concentrations in the California San Joaquin Valley (SJV). Radar wind profiler (RWP) and radio acoustic sounding system (RASS) data collected from 18 sites throughout Central California were used to estimate hourly mixing heights for a 3-month period and to create case studies of high-resolution diagnostic wind fields, which were used for trajectory and dispersion analyses. Data analyses show that PM episodes were characterized by an upper-level ridge of high pressure that generally produced light winds through the entire depth of the atmospheric boundary layer and low mixing heights compared with nonepisode days. Peak daytime mixing heights during episodes were -400 m above ground level (agl) compared with -800 m agl during nonepisodes. These episode/nonepisode differences were observed throughout the SJV. Dispersion modeling indicates that the range of influence of primary PM emitted in major population centers within the SJV ranged from -15 to 50 km. Trajectory analyses revealed that little intrabasin pollutant transport occurred among major population centers in the SJV; however, interbasin transport from the northern SJV and Sacramento regions into the San Francisco Bay Area (SFBA) was often observed. In addition, this analysis demonstrates the usefulness of integrating RWP/RASS measurements into data analyses and modeling to improve the understanding of meteorological processes that impact pollution, such as aloft transport and boundary layer evolution.

x concentrations in northern Qatar and transport from known emission source areas highlights the import ance of expanding the emission inventory for photochemical modeling. • Transport distances of 100 to 200 km per day we re observed during ozone episodes; thus, the potential for regional contributions to ozone is si gnificant. Based on analysis of observed winds, transport distances of 100 km or more per day we re likely during the August episodes and more than 200 km per day were likely during the November episode. Photochemical Modeling Findings • Ras Laffan area emissions alone contribute only a small portion of total observed ozone. Photochemical modeling of the RLIC emissions only produced peak concentrations that were 12 to 15 ppb above backgro und (boundary) concentrations. • Modeling to date does not account for the total obse rved ozone. The modeling system was only able to predict concentrations that were about 56% of those observed.