Space-time analysis of the Air Quality Model Evaluation International Initiative (AQMEII) Phase 1 air quality simulations

This study presents an evaluation of summertime ozone concentrations over North America (NA) and Europe (EU) using the database generated from Phase 1 of the Air Quality Model Evaluation International Initiative (AQMEII). The analysis focuses on identifying temporal and spatial features that can be used to stratify operational model evaluation metrics and to test the extent to which the various modeling systems can replicate the features seen in the observations. Using a synoptic map typing approach, it is demonstrated that model performance varies with meteorological conditions associated with specific synoptic-scale flow patterns over both eastern NA and EU. For example, the root mean square error of simulated daily maximum 8-hr ozone was twice as high when cloud fractions were high compared with when cloud fractions were low over eastern NA. Furthermore, results show that over both NA and EU the regional models participating in AQMEII were able to better reproduce the observed variance in ambient ozone levels than the global model used to specify chemical boundary conditions, although the variance simulated by almost all regional models is still less that the observed variance on all spatiotemporal scales. In addition, all modeling systems showed poor correlations with observed fluctuations on the intraday time scale over both NA and EU. Furthermore, a methodology is introduced to distinguish between locally influenced and regionally representative sites for the purpose of model evaluation. Results reveal that all models have worse model performance at locally influenced sites. Overall, the analyses presented in this paper show how observed temporal and spatial information can be used to stratify operational model performance statistics and to test the modeling systems’ ability to replicate observed temporal and spatial features, especially at scales the modeling systems are designed to capture.

Implications: The analyses presented in this paper demonstrate how observed temporal and spatial information can be used to stratify operational model performance and to test the modeling systems’ ability to replicate observed temporal and spatial features. Decisions for the improvement of regional air quality models should be based on the information derived from only regionally representative sites.     

Improved spatial allocation methodology for on-road mobile emissions

Emissions from automobiles and trucks operating on public roads represent a major portion of the air pollutants included in emission inventories. When emission data are prepared for air quality modeling studies, such as those supporting development of a State Implementation Plan, an emission processor matches the spatial and temporal resolution of the emissions to the requirements of the modeling study. However, the spatial location of vehicular emissions is not known and must be estimated. This paper presents a methodology for determining the spatial distribution of the roads belonging to a road class using geospatial data functions, such as those commonly provided by a geographic information system. Vehicle-miles traveled (VMT) are then allocated to medium-resolution (12 x 12-km) and fine-resolution (4 x 4-km) modeling grids using both this methodology and the existing top-down methodology, which uses population density. The results show a significant difference in the spatial distribution of VMT between these two methodologies. Based upon these results, we recommend using the road class-specific methodology in lieu of the population methodology for spatially allocating vehicular emissions for medium- and finer-resolution modeling grids.     

Improved Spatial Allocation Methodology for On-Road Mobile Emissions

ABSTRACT

Emissions from automobiles and trucks operating on public roads represent a major portion of the air pollutants included in emission inventories. When emission data are prepared for air quality modeling studies, such as those supporting development of a State Implementation Plan, an emission processor matches the spatial and temporal resolution of the emissions to the requirements of the modeling study. However, the spatial location of vehicular emissions is not known and must be estimated. This paper presents a methodology for determining the spatial distribution of the roads belonging to a road class using geospatial data functions, such as those commonly provided by a geographic information system. Vehicle-miles traveled (VMT) are then allocated to medium-resolution (12 x 12-km) and fine-resolution (4 x 4-km) modeling grids using both this methodology and the existing top-down methodology, which uses population density. The results show a significant difference in the spatial distribution of VMT between these two methodologies. Based upon these results, we recommend using the road class-specific methodology in lieu of the population methodology for spatially allocating vehicular emissions for medium- and finer-resolution modeling grids.     

National review of ambient air toxics observations

Ambient air observations of hazardous air pollutant (HAPs), also known as air toxics, derived from routine monitoring networks operated by states, local agencies, and tribes (SLTs), are analyzed to characterize national concentrations and risk across the nation for a representative subset of the 187 designated HAPs. Observations from the National Air Toxics Trend Sites (NATTS) network of 27 stations located in most major urban areas of the contiguous United States have provided a consistent record of HAPs that have been identified as posing the greatest risk since 2003 and have also captured similar concentration patterns of nearly 300 sites operated by SLTs. Relatively high concentration volatile organic compounds (VOCs) such as benzene, formaldehyde, and toluene exhibit the highest annual average concentration levels, typically ranging from 1 to 5 µg/m³. Halogenated (except for methylene chloride) and semivolatile organic compounds (SVOCs) and metals exhibit concentrations typically 2–3 orders of magnitude lower. Formaldehyde is the highest national risk driver based on estimated cancer risk and, nationally, has not exhibited significant changes in concentration, likely associated with the large pool of natural isoprene and formaldehyde emissions. Benzene, toluene, ethylbenzene, and 1,3-butadiene are ubiquitous VOC HAPs with large mobile source contributions that continue to exhibit declining concentrations over the last decade. Common chlorinated organic compounds such as ethylene dichloride and methylene chloride exhibit increasing concentrations. The variety of physical and chemical attributes and measurement technologies across 187 HAPs result in a broad range of method detection limits (MDLs) and cancer risk thresholds that challenge confidence in risk results for low concentration HAPs with MDLs near or greater than risk thresholds. From a national monitoring network perspective, the ability of the HAPs observational database to characterize the multiple pollutant and spatial scale patterns influencing exposure is severely limited and positioned to benefit by leveraging a variety of emerging measurement technologies.

Implications:?Ambient air toxics observation networks have limited ability to characterize the broad suite of hazardous air pollutants (HAPs) that affect exposures across multiple spatial scales. While our networks are best suited to capture major urban-scale signals of ubiquitous volatile organic compound HAPs, incorporation of sensing technologies that address regional and local-scale exposures should be pursued to address major gaps in spatial resolution. Caution should be exercised in interpreting HAPs observations based on data proximity to minimum detection limit and risk thresholds.     

A Discussion of Regulatory Requirements and Air Dispersion Modeling Approaches Applicable to U.S. Chemical Demilitarization Facilities

ABSTRACT

Owners of hazardous waste treatment, storage, and disposal facilities, and certain major air pollution sources, must conduct several separate ambient air dispersion modeling analyses before beginning construction of new facilities or modifying existing facilities. These analyses are critical components of the environmental permitting and facility certification processes and must be completed to the satisfaction of federal, state, and local regulatory authorities.

The U.S. Army has conducted air dispersion modeling for its proposed chemical agent disposal facilities to fulfill the following environmental regulatory and risk management requirements: (1) Resource Conservation and Recovery Act human health and ecological risk assessment analysis for the hazardous waste treatment and storage permit applications, (2) Quantitative Risk Assessment to support the site-specific risk management programs, and (3) Prevention of Significant Deterioration ambient air impact analysis for the air permit applications. The purpose of these air dispersion modeling studies is to show that the potential impacts on human health and the environment, due to operation of the chemical agent disposal facilities, are acceptable. This paper describes and compares the types of air dispersion models, modeling input data requirements, modeling algorithms, and approaches used to satisfy the three environmental regulatory and risk management requirements listed above. Although this paper discusses only one industry (i.e., chemical demilitarization), the information it contains could help those in other industries who need to communicate to the public the purpose and objectives of each modeling analysis. It may also be useful in integrating the results of each analysis into an overarching summary of compliance and potential risks.     

CAREAIR: A Computer Aided Modeling Toolbox to Generate and Analyze Emission Data in High Temporal and Spatial Resolution

ABSTRACT

For the evaluation of air quality improvement strategies, emission data in high temporal and spatial resolution is necessary, including all emission sources and all relevant pollutant species. Computer aided models are usually used to generate this emission data because it is not possible to obtain measurements from all sources, and, furthermore, a large amount of data has to be handled. For the development of emission modeling systems, a software tool called CAREAIR has been created. The intention of this paper is to introduce CAREAIR to the international community dealing with emission inventories and air quality improvement strategies.

CAREAIR is not just a single emission model but a flexible modeling toolbox. The database contains data and formulas for data manipulation, which is performed by using a set of flexible operators with different specifications. The emission calculation is carried out by combining several data manipulation operators. The CAREAIR modeling toolbox allows model implementation for the calculation of emissions from different pollutants in a high spatial and temporal resolution. The application of CAREAIR within various investigation projects in Germany, Europe, and Nigeria shows that CAREAIR is an appropriate instrument for the development of flexible emission models by meeting the various demands of these projects. The function and the data structures of this modeling toolbox are described and, towards the end of the paper, an example of an emission calculation with CAREAIR is given.     

Real-time ozone mapping using a regression-interpolation hybrid approach, applied to Tucson, Arizona

Real-time ozone (O3) maps, intended for public access and mass media, are generated from spatially interpolating (i.e., kriging) sparse monitoring data and are typically characterized by over-smoothed surfaces that inadequately represent local-scale spatial patterns (e.g., averaged over 1 km2). In this paper, a hybrid regression-interpolation methodology is developed to enhance the representation of local-scale spatiotemporal patterns with an application to Tucson, Arizona. The mapping of local patterns is enhanced with pre-interpolation regression modeling of local-scale deviation-from-mean variability, preserving variation in the monitor data that is ubiquitous across the modeling domain (i.e., the areal mean). The model is trained on several years of deviation-from-mean hourly O3 data, and predictor variables are developed using theoretically and empirically derived proxy regression variables. The regression model explains a significant proportion of the variation in the data (r2 = 0.54), with an average error of 7.1 ppb. When augmented with the areal mean, the r2 of the pre-interpolation model increases to 0.847. Model residuals are then spatially interpolated to the extents of the modeling domain. Final concentration estimate maps are the summation of areal mean, regression, and spatially interpolated surfaces, preserving absolute values at monitor locations.     

Pragmatic estimation of a spatio-temporal air quality model with irregular monitoring data

Statistical analyses of health effects of air pollution have increasingly used GIS-based covariates for prediction of ambient air quality in “land use” regression models. More recently these spatial regression models have accounted for spatial correlation structure in combining monitoring data with land use covariates. We present a flexible spatio-temporal modeling framework and pragmatic, multi-step estimation procedure that accommodates essentially arbitrary patterns of missing data with respect to an ideally complete space by time matrix of observations on a network of monitoring sites. The methodology incorporates a model for smooth temporal trends with coefficients varying in space according to Partial Least Squares regressions on a large set of geographic covariates and nonstationary modeling of spatio-temporal residuals from these regressions. This work was developed to provide spatial point predictions of PM_2.5 concentrations for the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air) using irregular monitoring data derived from the AQS regulatory monitoring network and supplemental short-time scale monitoring campaigns conducted to better predict intra-urban variation in air quality. We demonstrate the interpretation and accuracy of this methodology in modeling data from 2000 through 2006 in six U.S. metropolitan areas and establish a basis for likelihood-based estimation.     

Models for gas/particle partitioning,transformation and air/water surface exchange of PCBs and PCDD/Fs in CMAQ

We have added the capability to simulate polychlorinated biphenyls (PCBs) and polychlorinated dibenzo [p] dioxins and polychlorinated dibenzo-furans (PCDD/Fs) to the Community Multiscale Air Quality (CMAQ) modeling system, thus taking advantage of the latter's capability to simulate atmospheric advection, diffusion, gas-phase chemistry, cloud/precipitation, and aerosol processes. The modifications reported here include the addition to the CMAQ system of two gas/particle partitioning models options: the Junge–Pankow adsorption model and the K_OA absorption model, as well as chemical transformations and atmosphere/water surface exchange processes for these semi-volatile organics. Simulations for the purpose of model testing and validation were conducted for the years 2000 and 2002 on a domain covering most of North America. Both partitioning models give reasonable results when compared with available measurements. The model predictions of deposition and air concentrations also agree well with measurements. The modeling results also indicate that the long-range transport is important and anthropogenic emissions of PCBs and PCDD/Fs are dominant although surface exchange of PCBs may be important for some clean locations.     

Compilation of a database on the composition of anthropogenic VOC emissions for atmospheric modeling in Europe

To analyse and generate air pollution control strategies and policies, e.g. efficient abatement strategies or action plans that lead to a fulfilment of air quality aims, atmospheric dispersion models (CTMs) have to be used. These models include a chemical model, where the numerous volatile organic compounds (VOCs) species are lumped together in classes. On the other hand, emission inventories usually report only total non-methane VOC (NMVOC), but not a subdivision into these classes. Thus, VOC species profiles are needed that resolve total NMVOC emission data. The objective of this publication is to present the results of a compilation of VOC species profiles that dissolve total VOC into single-species profiles for all relevant anthropogenic emission source categories and the European situation. As in atmospheric dispersion models usually modules for generating biogenic emissions are directly included, only anthropogenic emissions are addressed. VOC species profiles for 87 emission source categories have been developed. The underlying data base can be used to generate the data for all chemical mechanisms. The species profiles have been generated using recent measurements and studies on VOC species resolution and thus represent the current state of knowledge in this area. The results can be used to create input data for atmospheric dispersion models in Europe.The profiles, especially those for solvent use, still show large uncertainties. There is still an enormous need for further measurements to achieve an improved species resolution. In addition, the solvent use directive and the DECOPAINT directive of the European Commission will result in a change of the composition of paints; more water-based and high-solid paints will be used; thus the species resolution will change drastically in the next years. Of course, the species resolution for combustion and production processes also requires further improvement.     

Modelling photochemical oxidant formation, transport, deposition and exposure of terrestrial ecosystems

The chemical processes responsible for production of photochemical oxidants within the troposphere have been the subject of laboratory and field study throughout the last three decades. During the same period, models to simulate the atmospheric chemistry, transport and deposition of ozone (O(3)) from individual urban sources and from regions have been developed. The models differ greatly in the complexity of chemical schemes, in the underlying meteorology and in spatial and temporal resolution. Input information from land use, spatial and temporally disaggregated emission inventories and meteorology have all improved considerably in recent years and are not fully implemented in current models. The development of control strategies in both North America and Europe to close the gaps between current exceedances of environmental limits, guide values, critical levels or loads and full compliance with these limits provides the focus for policy makers and the support agencies for the research. The models represent the only method of testing a range of control options in advance of implementation. This paper describes currently applied models of photochemical oxidant production and transport at global and regional scales and their ability to simulate individual episodes as well as photochemical oxidant climatology. The success of current models in quantifying the exposure of terrestrial surfaces and the population to potentially damaging O(3) concentrations (and dose) is examined. The analysis shows the degree to which the underlying processes and their application within the models limit the quality of the model products.     

Results of a Receptor Modeling Feasibility Study

This paper summarizes the results of an Electric Power Research Institute funded research effort to determine the feasibility of using receptor models for the apportionment of power plant contributions to air quality, deposition quality, and light extinction on local and regional scales. Sufficient information currently exists (or was developed during the course of this study) to establish feasibility for the apportionment of power plant contributions to local-scale air quality and to strongly suggest the usefulness of receptor modeling for regional-scale air quality, light extinction, and dry deposition quality apportionment. Insufficient information existed to determine whether or not receptor modeling can be useful for the allocation of wet deposition quality.

A series of seven future research recommendations were prepared for the purpose of advancing receptor modeling from theoretical feasibility to practical utility. Two recommendations address model evaluation: (1) prepare a user-oriented receptor model application and testing package, and (2) perform computer simulation testing of past, current, and proposed receptor model applications. Two recommendations address model development: (1) develop a receptor model requiring minimal information about source profiles, and (2) develop a “hybrid” model (i.e., a model that combines source and receptor oriented methods). Finally, three recommendations address source characterization: (1) develop procedures for individual particle characterization, (2) develop sampling and analysis methods for source profile determination, and (3) measure source profiles of coal- and oil-fired power plants, and other sources that confound identification of emissions from coal- and oilfired power plants.     

Interregional Exchanges of Air Pollution: Model Types and Applications

Regional scale air quality simulation models covering spatial scales of thousands of kilometers are finding increasing applications in studies of acid deposition and other air pollution problems. The purpose of this paper is to familiarize the nonexpert with the characteristics of the major types of interregional air quality models currently in use: Eulerian grid, statistical trajectory, and Lagrangian trajectory. The basic features, advantages, and disadvantages of each of these modeling approaches are summarized, as are the important limitations and problems associated with interregional modeling in general. Typical applications are illustrated using examples from the use of a representative Lagrangian trajectory model, ENAMAP, over the eastern North American area.     

Mass balance modelling of contaminants in river basins: application of the flexible matrix approach

It is useful to have available a variety of catchment-scale water quality models that range in complexity, spatial resolution and data requirements. In a previous paper [Warren, C., Mackay, D., Whelan, M., Fox, K., 2005. Mass balance modelling of contaminants in river basins: a flexible matrix approach. Chemosphere 61, 1458-1467] a series of simple to intermediately complex mass balance models was presented which can be used for tiered exposure assessments in river basins. The connectivity of the segments is expressed using a matrix that permits flexibility in application, enabling the model to be re-segmented and applied to different catchments as required. In this paper, the intermediate models, QWASI matrix-rate constant (QMX-R) and QWASI matrix-fugacity (QMX-F) are used to estimate concentrations of linear alkylbenzene sulfonates (LAS) in the rivers Aire and Calder, UK, and of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) in the Fraser River basin, Canada. The results compare satisfactorily with monitoring data, suggesting that these QWASI-based models for exposure and risk assessment may be applicable under data-limited conditions. The use of QWASI-based models for regulatory purposes in an evaluative river system is also discussed with reference to assessments of para-dichlorobenzene (pDCB), trichloroethylene (TCE), bis(2-ethylhexyl) phthalate (DEHP) and toluene. It is shown that multi-media QWASI model predictions can be usefully depicted graphically on chemical space diagrams and used to highlight regions in which advection, partitioning to sediments and volatilization may be important determinants of chemical fate in river systems.     

Air pollution dispersion within urban street canyons

A semi-empirical mathematical model, Urban Street Model (USM), is proposed to efficiently estimate the dispersion of vehicular air pollution in cities. This model describes urban building arrangements by combining building density, building heights and the permeability of building arrangements relative to wind flow. To estimate the level of air pollution in the city of Krasnoyarsk (in Eastern Siberia), the spatial distribution of pollutant concentrations off roadways is calculated using Markov's processes in USM. The USM-predicted numerical results were compared with field measurements and with results obtained from other frequently used models, CALINE-4 and OSPM. USM consistently yielded the best results. OSPM usually overestimated pollutant concentration values. CALINE-4 consistently underestimated these values. For OSPM, the maximum differences were 160% and for CALINE-4 about 400%. Permeability and building density are necessary parameters for accurately modeling urban air pollution and influencing regulatory requirements for building planning.     

Predictions of U.K. regulated power station contributions to regional air pollution and deposition: a model comparison exercise

Contributions of the emissions from a U.K. regulated fossil-fuel power station to regional air pollution and deposition are estimated using four air quality modeling systems for the year 2003. The modeling systems vary in complexity and emphasis in the way they treat atmospheric and chemical processes, and include the Community Multiscale Air Quality (CMAQ) modeling system in its versions 4.6 and 4.7, a nested modeling system that combines long- and short-range impacts (referred to as TRACK-ADMS [Trajectory Model with Atmospheric Chemical Kinetics-Atmospheric Dispersion Modelling System]), and the Fine Resolution Atmospheric Multi-pollutant Exchange (FRAME) model. An evaluation of the baseline calculations against U.K. monitoring network data is performed. The CMAQ modeling system version 4.6 data set is selected as the reference data set for the model footprint comparison. The annual mean air concentration and total deposition footprints are summarized for each modeling system. The footprints of the power station emissions can account for a significant fraction of the local impacts for some species (e.g., more than 50% for SO2 air concentration and non-sea-salt sulfur deposition close to the source) for 2003. The spatial correlation and the coefficient of variation of the root mean square error (CVRMSE) are calculated between each model footprint and that calculated by the CMAQ modeling system version 4.6. The correlation coefficient quantifies model agreement in terms of spatial patterns, and the CVRMSE measures the magnitude of the difference between model footprints. Possible reasons for the differences between model results are discussed. Finally, implications and recommendations for the regulatory assessment of the impact of major industrial sources using regional air quality modeling systems are discussed in the light of results from this case study.     

A spatial multicriteria model for determining air pollution at sample locations

Atmospheric pollution in urban centers has been one of the main causes of human illness related to the respiratory and circulatory system. Efficient monitoring of air quality is a source of information for environmental management and public health. This study investigates the spatial patterns of atmospheric pollution using a spatial multicriteria model that helps target locations for air pollution monitoring sites. The main objective was to identify high-priority areas for measuring human exposures to air pollutants as they relate to emission sources. The method proved to be viable and flexible in its application to various areas.

Implications:?Spatial multicriteria models provide a tool for air pollution management in urban areas. Analytic hierarchy process (AHP) modeling can help with the process of prioritizing monitoring site locations and minimizing costs.