Application Of Air Quality Models Research Articles

A comparative analysis of two highly spatially resolved European atmospheric emission inventories

A reliable emissions inventory is highly important for air quality modelling applications, especially at regional or local scales, which require high resolutions. Consequently, higher resolution emission inventories have been developed that are suitable for regional air quality modelling.This research performs an inter-comparative analysis of different spatial disaggregation methodologies of atmospheric emission inventories. This study is based on two different European emission inventories with different spatial resolutions: 1) the EMEP (European Monitoring and Evaluation Programme) inventory and 2) an emission inventory developed by the TNO (Netherlands Organisation for Applied Scientific Research). These two emission inventories were converted into three distinct gridded emission datasets as follows: (i) the EMEP emission inventory was disaggregated by area (EMEParea) and (ii) following a more complex methodology (HERMES-DIS – High-Elective Resolution Modelling Emissions System – DISaggregation module) to understand and evaluate the influence of different disaggregation methods; and (iii) the TNO gridded emissions, which are based on different emission data sources and different disaggregation methods. A predefined common grid with a spatial resolution of 12 × 12 km2 was used to compare the three datasets spatially.The inter-comparative analysis was performed by source sector (SNAP – Selected Nomenclature for Air Pollution) with emission totals for selected pollutants. It included the computation of difference maps (to focus on the spatial variability of emission differences) and a linear regression analysis to calculate the coefficients of determination and to quantitatively measure differences.From the spatial analysis, greater differences were found for residential/commercial combustion (SNAP02), solvent use (SNAP06) and road transport (SNAP07). These findings were related to the different spatial disaggregation that was conducted by the TNO and HERMES-DIS for the first two sectors and to the distinct data sources that were used by the TNO and HERMES-DIS for road transport.Regarding the regression analysis, the greatest correlation occurred between the EMEParea and HERMES-DIS because the latter is derived from the first, which does not occur for the TNO emissions. The greatest correlations were encountered for agriculture NH3 emissions, due to the common use of the CORINE Land Cover database for disaggregation. The point source emissions (energy industries, industrial processes, industrial combustion and extraction/distribution of fossil fuels) resulted in the lowest coefficients of determination. The spatial variability of SOx differed among the emissions that were obtained from the different disaggregation methods.In conclusion, HERMES-DIS and TNO are two distinct emission inventories, both very well discretized and detailed, suitable for air quality modelling. However, the different databases and distinct disaggregation methodologies that were used certainly result in different spatial emission patterns. This fact should be considered when applying regional atmospheric chemical transport models. Future work will focus on the evaluation of air quality models performance and sensitivity to these spatial discrepancies in emission inventories. Air quality modelling will benefit from the availability of appropriate resolution, consistent and reliable emission inventories.

The Phoenix Evening Transition Flow Experiment (TRANSFLEX)

Motivated by air quality and numerical modelling applications as well as recent theoretical advancements in the topic, a field experiment, dubbed transition flow experiment, was conducted in Phoenix, Arizona to study the evening transition in complex terrain (shift of winds from upslope to downslope). Two scenarios were considered: (i) the flow reversal due to a change of buoyancy of a cooled slab of air near the ground, and (ii) the formation of a transition front. A suite of in-situ flow, turbulence and particulate matter (PM) concentration sensors, vertically profiling tethered balloons and remote sensors were deployed, and a mesoscale numerical model provided guidance for interpreting observations. The results were consistent with the front formation mechanism, where it was also found that enhanced turbulence associated with the front increases the local PM concentration. During the transition period the flow adjustment was complex, involving the arrival of multiple fronts from different slopes, directional shear between fronts and episodic turbulent mixing events. The upward momentum diffusion from the incipient downslope flow was small because of stable stratification near the ground, and full establishment of downslope flow occurred over several hours following sunset. Episodic frontal events pose challenges to the modelling of the evening transition in complex terrain, requiring conditional parametrizations for subgrid scales. The observed increase of PM concentration during the evening transition has significant implications for the regulatory enforcement of PM standards for the area.

Upgrade of the DAUMOD atmospheric dispersion model to estimate urban background NO2 concentrations

Ambient concentrations of nitrogen dioxide (NO2) resulting from the emissions of nitrogen oxides (NOx) and volatile organic compounds (VOCs) in an urban area may cause adverse impacts on the human health and the natural environment if they exceed the air quality standards. The evaluation of NO2 can be achieved through the application of air quality models including photochemical reactions. DAUMOD is a simple urban scale atmospheric dispersion model which was originally developed to estimate urban background concentrations of inert species. In order to allow the estimation of NO2 concentrations in an urban atmosphere, the DAUMOD model has been recently coupled to the Generic Reaction Set (GRS), a simplified photochemical scheme. This work presents the development of the DAUMOD-GRS model and its first application in the city of Buenos Aires considering high resolution area source NOx and VOC emissions recently obtained for the area. Estimated hourly NO2 concentrations are compared with the observations from a campaign carried out at a green open area within the city in winter 2001. Results show a good model performance, with NMSE=0.49, FA2=0.676 and FB=−0.097. DAUMOD-GRS is applied to obtain the spatial distribution of annual NO2 concentrations in the city. The maximum value is 36ppb, indicating that annual NO2 concentrations in Buenos Aires are below the Air Quality Standard (53ppb). In addition, hourly ozone concentrations estimated by DAUMOD-GRS are compared with observed values, giving NMSE=0.38, FA2=0.684 and FB=−0.225.

A tool to evaluate air quality model performances in regulatory applications

This paper describes the details of the DELTA Tool and Benchmarking service for air quality models, recently developed in the framework of FAIRMODE (Forum for Air Quality Modelling in Europe). One of the main objectives of the FAIRMODE activities is the development of a procedure for the evaluation and benchmarking of air quality modelling applications for regulatory purposes. The DELTA Tool is a specific software which provides summary statistics (i.e. BIAS, RMSE, correlation coefficient) as well as scatter-plots, time series plots, Taylor, Target and other diagrams providing an overview of the quality of model results with respect to monitored data. Moreover, the benchmarking service implemented in DELTA produces summary reports containing performance indicators related to a given model application in the frame of the EU Air Quality Directive (AQD, 2008). This work describes the structure of the DELTA tool and template for reporting model performances. Some examples of application are also briefly presented.

Performance criteria to evaluate air quality modeling applications

A set of statistical indicators fit for air quality model evaluation is selected based on experience and literature: The Root Mean Square Error (RMSE), the bias, the Standard Deviation (SD) and the correlation coefficient (R). Among these the RMSE is proposed as the key one for the description of the model skill. Model Performance Criteria (MPC) to investigate whether model results are ‘good enough’ for a given application are calculated based on the observation uncertainty (U). The basic concept is to allow for model results a similar margin of tolerance (in terms of uncertainty) as for observations. U is pollutant, concentration level and station dependent, therefore the proposed MPC are normalized by U. Some composite diagrams are adapted or introduced to visualize model performance in terms of the proposed MPC and are illustrated in a real modeling application. The Target diagram, used to visualize the RMSE, is adapted with a new normalization on its axis, while complementary diagrams are proposed. In this first application the dependence of U on concentrations level is ignored, and an assumption on the pollutant dependent relative error is made. The advantages of this new approach are finally described.

Performance criteria for the benchmarking of air quality model regulatory applications: the 'target' approach

The definition of appropriate performance criteria is one of the key issues for the benchmarking of air quality models in regulatory applications. As part of the FAIRMODE benchmarking activities (Thunis et al., 2010), suitable criteria for air quality modelling in the frame of the EU air quality directive (AQD) 2008 are proposed and tested. The suggested approach builds on the target indicator (Jolliff et al., 2009) as support to the relative directive error, the current official statistical parameter as defined in the AQD (EEA, 2011), for quantitatively estimating model performances in air quality modelling applications. This study describes the advantages of using the target compared to the actual limitations of RDE and addresses the main links between the target and some ‘traditional’ statistical indicators (MFB, R, FAC2, σ ). It also describes the application of this methodology to NO 2 , O 3 and PM 10 concentrations on three different model-observations datasets. Among these datasets two focus on the urban areas of Madrid and London and include modelled results provided by the air quality models CMAQ and ADMS-Urban for years 2007 and 2008 respectively. One other dataset (POMI) covering the Po valley and including multiple model results has also been tested for year 2005.

Quality Assurance Decisions with Air Models: A Case Study of Imputation of Missing Input Data Using EPA’s Multi-layer Model

Environmental models are frequently used within regulatory and policy frameworks to estimate environmental metrics that are difficult or impossible to physically measure. As important decision tools, the uncertainty associated with the model outputs should impact their use in informing regulatory decisions and scientific inferences. In this paper, we present a case study illustrating a process for dealing with a key issue in the use and application of air quality models, the additional error in annual mean aggregations resulting from imputation of missing data from model data sets. The case study is based on the US Environmental Protection Agency’s Multi-layer Model, which estimates the hourly dry deposition velocity of air pollutants based on hourly measurements of meteorology and site characteristics. A simulation was implemented to evaluate the effect of substituting historical hour-specific average values for missing model deposition velocity predictions on annual mean aggregations. Sensitivity studies were performed to test the effects of different missing data patterns and evaluate the relative impact of the substitution procedure on annual mean SO2 deposition velocity estimates. The substitution procedure was shown to result generally in long-term unbiased estimates of the annual mean and contributed less than 20% additional error to the estimate even when all data were missing. Consequently, it may be possible to use the historical record of deposition velocities to provide reasonably accurate and unbiased annual estimates of deposition velocities for years without meteorological measurements.

Air Pollution: Salt Mist Is the Right Seasoning for Ozone

Vol. 116, No. 7 EnvironewsOpen AccessAir Pollution: Salt Mist Is the Right Seasoning for Ozone Carol Potera Carol Potera Search for more papers by this author Published:1 July 2008https://doi.org/10.1289/ehp.116-a288Cited by:2AboutSectionsPDF ToolsDownload CitationsTrack Citations ShareShare onFacebookTwitterLinked InReddit Shipping ports face a newly discovered air pollution problem—the production of the ozone precursor nitryl chloride. Nitryl chloride was detected for the first time in the lowest part of the Earth’s atmosphere by a team from the National Oceanic and Atmospheric Administration (NOAA) that was monitoring air quality in Galveston Bay to understand why nearby Houston, Texas, has one of the worst air pollution problems in the nation. Salts in ocean mists were thought to be relatively inert until the connection to ozone was uncovered. “People never before thought that nitryl chloride was important,” says James Roberts, a NOAA research chemist and the team’s coordinator.In the summer of 2006, the researchers used chemical ionization mass spectrometry to detect trace levels of airborne chemicals, including nitryl chloride. They found that when ship exhaust plumes rich in nitrogen oxides (NOx) meet ocean air at night, unexpectedly high levels of nitryl chloride form due to the NOx species dinitrogen pentoxide combining with chloride in sea mist. After the sun comes up, this buildup of photoactive nitryl chloride splits into chlorine atoms and nitrogen dioxide. These compounds then accelerate the production of ozone, a key component of smog.The amount of nitryl chloride measured by Roberts and colleagues—as high as 650 ppt by volume, or about 15% of total reactive nitrogen species from ship exhaust—is much greater than that estimated by standard air pollution models, which have taken into account neither the heightened presence of nitryl chloride around ports nor its importance in forming ozone. “This preliminary study indicates that nitryl chloride chemistry could make a significant contribution—up to ten to thirty percent—to ozone production during the morning hours in Houston,” says Roberts. These results were published in the May 2008 issue of Nature Geoscience.The study findings point to the need to control NOx emissions from fossil fuel combustion in coastal cities, says Roberts. He adds that one solution may be to require docked ships to use local electrical power, rather than burning diesel fuel, to generate electricity. Some ports have begun to pursue this course of action [see “Ports in a Storm,” EHP 114:A222–A231 (2006)]. The 10-year $750-million Middle Harbor Redevelopment Project proposed by the Port of Long Beach (California) would, among other pollution mitigation strategies, provide shoreside electricity for docked vessels.About half the world’s population lives near coastlines where industrial pollution meets ocean air, Roberts says, so nitryl chloride could play a major role in air quality worldwide. The same reaction likely occurs inland, where chloride-containing aerosols drive the chemical reaction. Inland sources of chloride include natural soil salts such as calcium chloride and de-icing compounds spread on winter roads.“We don’t know how widespread nitryl chloride is as a source of ozone pollution,” says Roberts. The health consequences of nitryl chloride itself are unknown. As for ozone, an April 2008 report by the National Research Council, Estimating Mortality Risk Reduction and Economic Benefits from Controlling Ozone Air Pollution, links even short-term exposure with premature death.“This is the first field measurement of nitryl chloride, and it’s very exciting,” says Barbara Finlayson-Pitts, a professor of chemistry at the University of California, Irvine. Experiments in her laboratory 20 years ago first showed that mixing sodium chloride with dinitrogen pentoxide in the dark generated nitryl chloride. “The data will be extremely useful for further development and application of air quality models for coastal urban areas,” she says.The NOx in ship exhaust combines with chloride in sea mist to produce a potent ozone precursorFiguresReferencesRelatedDetailsCited by Jiang J and Li D (2016) Theoretical analysis and experimental confirmation of exhaust temperature control for diesel vehicle NOx emissions reduction, Applied Energy, 10.1016/j.apenergy.2016.04.096, 174, (232-244), Online publication date: 1-Jul-2016. Fleer M and Verkuijl B (2014) Optimization of the use of a chiral bio-based building block for the manufacture of DHPPA, a key intermediate for propionate herbicides, Green Chemistry, 10.1039/C4GC00797B, 16:8, (3993) Vol. 116, No. 7 July 2008Metrics About Article Metrics Publication History Originally published1 July 2008Published in print1 July 2008 Financial disclosuresPDF download License information EHP is an open-access journal published with support from the National Institute of Environmental Health Sciences, National Institutes of Health. All content is public domain unless otherwise noted. Note to readers with disabilities EHP strives to ensure that all journal content is accessible to all readers. However, some figures and Supplemental Material published in EHP articles may not conform to 508 standards due to the complexity of the information being presented. If you need assistance accessing journal content, please contact [email protected]. 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Using Prognostic Model–Generated Meteorological Output in the AERMOD Dispersion Model: An Illustrative Application in Philadelphia, PA

In this study, we introduce the prospect of using prognostic model–generated meteorological output as input to steady–state dispersion models by identifying possible advantages and disadvantages and by presenting a comparative analysis. Because output from prognostic meteorological models is now routinely available and is used for Eulerian and Lagrangian air quality modeling applications, we explore the possibility of using such data in lieu of traditional National Weather Service (NWS) data for dispersion models. We apply these data in an urban application where comparisons can be made between the two meteorological input data types. Using the U.S. Environment Protection Agency’s American Meteorological Society/U.S. Environmental Protection Agency Regulatory Model (AERMOD) air quality dispersion model, hourly and annual average concentrations of benzene are estimated for the Philadelphia, PA, area using both hourly MM5 model–generated meteorological output and meteorological data taken from the NWS site at the Philadelphia International Airport. Our intent is to stimulate a discussion of the relevant issues and inspire future work that examines many of the questions raised in this paper.

On the estimation of characteristic indoor air quality parameters using analytical and numerical methods

Indoor exposure to air contaminants penetrating from the outdoor environment depends on a number of key processes and parameters such as the ventilation rate, the geometric characteristics of the indoor environment, the outdoor concentration and the indoor removal mechanisms. In this study two alternative methods are used, an analytical and a numerical one, in order to study the time lag and the reduction of the variances of the indoor concentrations, and to estimate the deposition rate of the air contaminants in the indoor environment employing both indoor and outdoor measurements of air contaminants. The analytical method is based on a solution of the mass balance equation involving an outdoor concentration pulse which varies sinusoidally with the time, while the numerical method involves the application of the MIAQ indoor air quality model assuming a triangular pulse. The ratio of the fluctuation of the indoor concentrations to the outdoor ones and the time lag were estimated for different values of the deposition velocity, the ventilation rate and the duration of the outdoor pulse. Results have showed that the time lag between the indoor and outdoor concentrations is inversely proportional to the deposition and ventilation rates, while is proportional to the duration of the outdoor pulse. The decrease of the ventilation and the deposition rate results in a rapid decrement of the variance ratio of indoor to outdoor concentrations and to an increment of the variance ratio, respectively. The methods presented here can be applied for gaseous species as well as for particulate matter. The nomograms and theoretical relationships that resulted from the simulation results and the analytical methods respectively were used in order to study indoor air phenomena. In particular they were used for the estimation of SO2 deposition rate. Implications of the studied parameters to exposure studies were estimated by calculating the ratio of the indoor exposure to the exposure outdoors. Limitations of the methods were explored by testing various scenarios which are usually met in the indoor environment. Strong indoor emissions, intense chemistry and varying ventilation rates (opening and closing of the windows) were found to radically influence the time lag and fluctuation ratios.

Evaluating the use of outputs from comprehensive meteorological models in air quality modeling applications

Currently used dispersion models, such as the AMS/EPA Regulatory Model (AERMOD), process routinely available meteorological observations to construct model inputs. Thus, model estimates of concentrations depend on the availability and quality of meteorological observations, as well as the specification of surface characteristics at the observing site. We can be less reliant on these meteorological observations by using outputs from prognostic models, which are routinely run by the National Oceanic and Atmospheric Administration (NOAA). The forecast fields are available daily over a grid system that covers all of the United States. These model outputs can be readily accessed and used for dispersion applications to construct model inputs with little processing. This study examines the usefulness of these outputs through the relative performance of a dispersion model that has input requirements similar to those of AERMOD. The dispersion model was used to simulate observed tracer concentrations from a Tracer Field Study conducted in Wilmington, California in 2004 using four different sources of inputs: (1) onsite measurements; (2) National Weather Service measurements from a nearby airport; (3) readily available forecast model outputs from the Eta Model; and (4) readily available and more spatially resolved forecast model outputs from the MM5 prognostic model. The comparison of the results from these simulations indicate that comprehensive models, such as MM5 and Eta, have the potential of providing adequate meteorological inputs for currently used short-range dispersion models such as AERMOD.

Air Quality Modeling and Decisions for Ozone Reduction Strategies

Despite the widespread application of photochemical air quality models (AQMs) in U.S. state implementation planning (SIP) for attainment of the ambient ozone standard, documentation for the reliability of projections has remained highly subjective. An “idealized” evaluation framework is proposed that provides a means for assessing reliability. Applied to 18 cases of regulatory modeling in the early 1990s in North America, a comparative review of these applications is reported. The intercomparisons suggest that more than two thirds of these AQM applications suffered from having inadequate air quality and meteorological databases. Emissions representations often were unreliable; uncertainties were too high. More than two thirds of the performance evaluation efforts were judged to be substandard compared with idealized goals. Meteorological conditions chosen according regulatory guidelines were limited to one or two cases and tended to be similar, thus limiting the extent to which public policy makers could be confident that the emission controls adopted would yield attainment for a broad range of adverse atmospheric conditions. More than half of the studies reviewed did not give sufficient attention to addressing the potential for compensating errors. Corroborative analyses were conducted in only one of the 18 studies reviewed. Insufficient attention was given to the estimation of model and/or input database errors, uncertainties, or variability in all of the cases examined. However, recent SIP and policy‐related regional modeling provides evidence of substantial improvements in the underlying science and available modeling systems used for regulatory decision making. Nevertheless, the availability of suitable databases to support increasingly sophisticated modeling continues to be a concern for many locations. Thus, AQM results may still be subject to significant uncertainties. The evaluative process used here provides a framework for modelers and public policy makers to assess the adequacy of contemporary and future modeling work.

Application of air quality models to public health analysis

Although measurements of individual exposure remain the “gold standard” for evaluating health risks associated with air pollution, atmospheric models have become important tools for understanding the links between energy use, air pollution, and public health. The spatial and temporal coverage of model data typically far exceeds that of measurement data; the cost of simulating air pollution concentrations with a model is low compared with the equipment and personnel costs to operate a network of measurement stations; and models permit analysis of future projections and implications of air quality management policy. Still, important limitations remain. No model is perfect, and data quality is limited by model accuracy, meteorological input, and quantification of emissions. Exploiting the strengths of models in public health analysis, while appropriately dealing with model uncertainty, poses challenges that span public health and atmospheric science disciplines. Here we review past studies in which models have been employed in air pollution health analyses, and we discuss new directions in public health that capitalize on atmospheric model strengths.

Estimation of biogenic emissions with satellite-derived land use and land cover data for air quality modeling of Houston-Galveston ozone nonattainment area

The Houston-Galveston Area (HGA) is one of the most severe ozone non-attainment regions in the US. To study the effectiveness of controlling anthropogenic emissions to mitigate regional ozone nonattainment problems, it is necessary to utilize adequate datasets describing the environmental conditions that influence the photochemical reactivity of the ambient atmosphere. Compared to the anthropogenic emissions from point and mobile sources, there are large uncertainties in the locations and amounts of biogenic emissions. For regional air quality modeling applications, biogenic emissions are not directly measured but are usually estimated with meteorological data such as photo-synthetically active solar radiation, surface temperature, land type, and vegetation database. In this paper, we characterize these meteorological input parameters and two different land use land cover datasets available for HGA: the conventional biogenic vegetation/land use data and satellite-derived high-resolution land cover data. We describe the procedures used for the estimation of biogenic emissions with the satellite derived land cover data and leaf mass density information. Air quality model simulations were performed using both the original and the new biogenic emissions estimates. The results showed that there were considerable uncertainties in biogenic emissions inputs. Subsequently, ozone predictions were affected up to 10 ppb, but the magnitudes and locations of peak ozone varied each day depending on the upwind or downwind positions of the biogenic emission sources relative to the anthropogenic NO x and VOC sources. Although the assessment had limitations such as heterogeneity in the spatial resolutions, the study highlighted the significance of biogenic emissions uncertainty on air quality predictions. However, the study did not allow extrapolation of the directional changes in air quality corresponding to the changes in LULC because the two datasets were based on vastly different LULC category definitions and uncertainties in the vegetation distributions.

A case study for assessing uncertainty in local-scale regulatory air quality modeling applications

In this paper, we expand upon established uncertainty analysis techniques to demonstrate a general method for assessing variability and uncertainty in Gaussian air pollutant dispersion modeling systems. To illustrate this method, we estimated variability and uncertainty in predicted hexavalent chromium concentrations generated by welding operations at a shipbuilding and repair facility in California. Using Monte Carlo statistical techniques, we propagated uncertainty across both ISCST3 and AERMOD, and estimated the contribution of variability and uncertainty from four model components: emissions, spatial and temporal allocation of emissions, model parameters, and meteorology. Our results indicated the 95% confidence interval uncertainty at each receptor spanned an order of magnitude. From a practical perspective uncertainty is most important at receptors with highest predicted concentrations. In this case study, emissions were the primary source of uncertainty. However, Gaussian models are also sensitive to location of emission releases, meteorology, and model parameters. Simplified modeling approaches may lead to errors in pollutant concentration estimates, especially in close proximity to emissions sources where predicted concentrations are highest.

Overview of the 1997 Southern California Ozone Study (SCOS97-NARSTO)

Abstract During the summer of 1997, the Southern California Ozone Study-NARSTO 1 (SCOS97-NARSTO) was conducted in order to update and improve the existing emission, meteorological, and air quality databases and model applications for representing urban-scale ozone episodes in Southern California, and to quantify the contributions of ozone generated from emissions in Southern California air basins to US and California ambient ozone standard exceedances in neighboring air basins. The SCOS97-NARSTO also examined the generation and evolution of typical late-summer and early fall aerosols in the Los Angeles area. This paper describes the study area and design of the field campaign, summarizes the field measurements, and reviews data analysis and modeling efforts.

Application of a parallel photochemical air quality model to the Campania region (southern Italy)

During most of the year, the concentrations of both primary and secondary air pollutants over the Campania region (southern Italy) do not comply with the Italian air quality standards. To gain insight into the chemical and meteorological processes that lead to high air pollutant concentrations over this area, the parallel package PNAM (Parallel Naples Airshed Model) has been developed, for the numerical simulation of photosmog episodes on urban and regional scale domains. PNAM has been applied to a photosmog episode which occurred on 26 July 1995. On this day, due to the stagnant conditions and the intensive solar radiation, a high ozone concentration was reported for the Naples basin. The performance of PNAM has been assessed by comparing measured air quality data with simulated data for O 3, NO, NO 2 and CO. PNAM was able to reproduce temporal and spatial characteristics of measured air quality data, although some discrepancies were evident, probably mainly due to the emission inventory, which was based only on total annual emissions.

Meteorological modeling for air-quality assessments

Meteorological fields are required inputs for air-quality models, but they can contain significant errors which contribute to uncertainties in simulations of airborne chemical species, aerosols and particulate matter. Atmospheric states can be diagnosed from observations or simulated by dynamical models (with or without four-dimensional data assimilation, FDDA). In general, diagnostic models are straightforward to operate, but obtaining sufficient observations to analyze regional-scale features is costly, may omit key variables and often lack sufficient spatial or temporal density to describe the fields adequately. Dynamical models, although still imperfect, have improved in recent years and are now widely accepted for many air-quality modeling applications. Examination of the current state of dynamical models used as meteorological pre-processors indicates that useful simulations for real cases are feasible for scales at least as fine as 1 km. Introduction of faster computers and practical FDDA techniques already allow simulations of regional episodes lasting up to 5–10 d with fine resolutions (5 km or less). As technology has improved, however, a need has developed for better parameterizations to represent vital physical processes, such as boundary layer fluxes, deep convection and clouds, at these finer grid scales. Future developments in meteorological modeling for air-quality applications will include advanced model physics and data assimilation, better coupling between meterological and chemical models, and could lead eventually to widespread use of fully integrated meteorological-chemical models for simulating and predicting air quality.

Actual car fleet emissions estimated from urban air quality measurements and street pollution models

A method to determine emissions from the actual car fleet under realistic driving conditions has been developed. The method is based on air quality measurements, traffic counts and inverse application of street air quality models. Many pollutants are of importance for assessing the adverse impact of the air pollution, e.g. NO 2, CO, lead, VOCs and particulate matter. Aromatic VOCs are of special great concern due to their adverse health effects. Measurements of benzene, toluene and xylenes were carried out in central Copenhagen since 1994. Significant correlation was observed between VOCs and CO concentrations, indicating that the petrol engine vehicles are the major sources of VOC air pollution in central Copenhagen. Hourly mean concentrations of benzene were observed to reach values of up to 20 ppb, what is critically high according to the WHOs recommendations. Based on inverse model calculation of dispersion of pollutants in street canyons, an average emission factor of benzene for the fleet of petrol fuelled vehicles was estimated to be 0.38 g/km in 1994 and 0.11 in 1997. This decrease was caused by the reduction of benzene content in Danish petrol since summer 1995 and increasing percentage of cars equipped with three-way catalysts. The emission factors for benzene for diesel-fuelled vehicles were low.

Impact of inert organic nitrate formation on ground‐level ozone in a regional air quality model using the Carbon Bond Mechanism 4

A regional air quality model is used to assess the impact of inert organic nitrate formation on ground‐level ozone in the eastern United States during summer. The chemical mechanism used is the Carbon Bond Mechanism 4 (CBM4), which is widely used by regulatory agencies in the United States in air quality modeling applications. Recently, modifications were made to the reaction mechanism involving the organic peroxy radicals which form inert organic nitrates without a critical scientific review of the effects of these changes. In this study, we demonstrate for the first time that the simulated large‐scale distribution of ground‐level ozone is extremely sensitive to these mechanism changes. Inclusion of radical‐radical reactions involving the organic peroxy radicals suppresses inert organic nitrate formation, and leads to significant increases in nitrogen oxide levels over large parts of the model domain. As a consequence of increased rates of ozone photochemical production, ozone mixing ratios are enhanced by as much 10–25 ppbv when these additional radical termination pathways are considered in the model.