Dilution of exhaust from a rooftop stack on a cubical building in an urban environment

The dispersion of a tracer gas emitted from a short stack on a cubical building was investigated using field and wind tunnel experiments. Air samples were obtained on the roof and the leeward side of the building. Dilution data were compared with estimates of minimum dilution (D_min) obtained with design formulas of Wilson/Chui/Lamb and Halitsky. The Halitsky model produced conservative predictions of D_min. Estimates of D_min obtained with the Wilson/Chui/Lamb model were in reasonable agreement with the field data. Wind tunnel dilution values were usually within a factor of two of the field data. The accuracy of the wind tunnel data improved as distance from the source increased. Wind tunnel dilution was found to be strongly dependent on the ratio of exhaust speed to wind speed, M, particularly for M values in the range of 2–4. This dependence is believed to be associated with the wind tunnel modelling of the stack exhaust, and is thus probably not a feature of the full-scale situation.     

Simulation of the evolution of particle size distributions in a vehicle exhaust plume with unconfined dilution by ambient air

Over the past several years, numerous studies have linked ambient concentrations of particulate matter (PM) to adverse health effects, and more recent studies have identified PM size and surface area as important factors in determining the health effects of PM. This study contributes to a better understanding of the evolution of particle size distributions in exhaust plumes with unconfined dilution by ambient air. It combines computational fluid dynamics (CFD) with an aerosol dynamics model to examine the effects of different streamlines in an exhaust plume, ambient particle size distributions, and vehicle and wind speed on the particle size distribution in an exhaust plume. CFD was used to calculate the flow field and gas mixing for unconfined dilution of a vehicle exhaust plume, and the calculated dilution ratios were then used as input to the aerosol dynamics simulation. The results of the study show that vehicle speed affected the particle size distribution of an exhaust plume because increasing vehicle speed caused more rapid dilution and inhibited coagulation. Ambient particle size distributions had an effect on the smaller sized particles (approximately 10 nm range under some conditions) and larger sized particles (>2 microm) of the particle size distribution. The ambient air particle size distribution affects the larger sizes of the exhaust plume because vehicle exhaust typically contains few particles larger than 2 microm. Finally, the location of a streamline in the exhaust plume had little effect on the particle size distribution; the particle size distribution along any streamline at a distance x differed by less than 5% from the particle size distributions along any other streamline at distance x.     

An evaluation of three empirical air-to-leaf models for polychlorinated dibenzo-p-dioxins and dibenzofurans

Three empirical air-to-leaf models for estimating grass concentrations of polychlorinated dibenzo-p-dioxins and dibenzofurans (abbreviated dioxins and furans) from air concentrations of these compounds are described and tested against two field data sets. All are empirical in that they are founded on simplistic bioconcentration and related approaches which rely on field data for their parameterization. One of the models, identified as the EPA Model, partitions the total air concentration into vapor and particle phases, and separately models the impact of both. A second model addresses only the vapor phase; grass concentrations are modeled as a function of vapor deposition. For the third model, it is assumed that the grass plants "scavenge" a fixed volume of air of dioxins, and hence grass concentrations are modeled as a simple product of total air concentration and a constant scavenging coefficient. Field data from two sites, a rural and an industrial site in the United Kingdom, included concurrent measurements of dioxins in air and field grass, and dioxin and furan depositions, for one 6-week sampling period. Principal findings include: (1) the EPA Model underpredicted grass concentrations at the rural field site by a factor of 2, while the Scavenging Model underpredicted grass concentrations by a factor of 3.8, and the Vapor Deposition Model significantly underpredicted grass concentrations (by a factor greater than 10), (2) the presence of high soil concentrations for some of the dioxins and furans at the industrial site appears to have caused higher grass concentrations and confounded the air-to-plant modeling exercise, (3) the Scavenging Model could be calibrated to the data set; however, a key premise of this model that vapor and particle phase dioxins equally impact the plants, is not supported by the field data, (4) measured depositions are highly correlated to but systematically lower than modeled depositions, which could be due to modeling assumptions or a systematic measurement bias.     

Evaluation and comparison of multimedia mass balance models of chemical fate: application of EUSES and ChemCAN to 68 chemicals in Japan

The European Union System for Evaluation of Substances (EUSES) and the ChemCAN chemical fate model are applied to describe the fate of 68 chemicals on two spatial scales in Japan. Emission information on the chemicals has been obtained from Japan's Pollutant Release and Transfer Registry and available monitoring data gathered from government reports. Environmental concentrations calculated by the two models for the four primary environmental media of air, water, soil and sediment agree within a factor of 3 for over 70% of the data, and within a factor of 10 for over 87% of the data. Reasons for certain large discrepancies are discussed. Concentrations calculated by the models are generally consistent with the lower range of concentrations that are observed in the environment. Agreement between modeled and observed concentrations is considerably improved by including an estimate of the advective input of chemicals in air from outside Japan. The agreement between the EUSES and ChemCAN models suggests that results of individual chemical assessments are not likely to be significantly affected by the choice of chemical fate model. Primary sources of discrepancy between modeled and observed concentrations are believed to be uncertainties in emission rates, degradation half-lives, and the lack of data on advective inflow of contaminants in air.     

Measurement of ultrafine particle size distributions from coal-, oil-, and gas-fired stationary combustion sources

Currently, we have limited knowledge of the physical and chemical properties of emitted primary combustion aerosols and the changes in those properties caused by nucleation, condensation growth of volatile species, and particle coagulations under dilution and cooling in the ambient air. A dilution chamber was deployed to sample exhaust from a pilot-scale furnace burning various fuels at a nominal heat input rate of 160 kW/h(-1) and 3% excess oxygen. The formation mechanisms of particles smaller than 420 nm in electrical mobility diameter were experimentally investigated by measurement with a Scanning Mobility Particle Sizer (SMPS) as a function of aging times, dilution air ratios, combustion exhaust temperatures, and fuel types. Particle formation in the dilution process is a complex mixture of nucleation, coagulation, and condensational growth, depending on the concentrations of available condensable species and solid or liquid particles (such as soot, ash) in combustion exhausts. The measured particle size distributions in number concentrations measured show peaks of particle number concentrations for medium sulfur bituminous coal, No. 6 fuel oil, and natural gas at 40-50 nm, 70-100 nm, and 15-25 nm, respectively. For No. 6 fuel oil and coal, the particle number concentration is constant in the range of a dilution air ratio of 50, but the number decreases as the dilution air ratio decreases to 10. However, for natural gas, the particle number concentration is higher at a dilution air ratio of 10 and decreases at dilution air ratios of 20-50. At a dilution air ratio of 10, severe particle coagulation occurs in a relatively short time. Samples taken at different combustion exhaust temperatures for these fuel types show higher particle number concentrations at 645 K than at 450 K. As the aging time of particles increases, the particles increase in size and the number concentrations decrease. The largest gradient of particle number distribution occurs within the first 10 sec after dilution but shows only minor differences between 10 and 80 sec. The lifetimes of the ultrafine particles are relatively short, with a scale on the order of a few seconds. Results from this study suggest that an aging time of 10 sec and a dilution air ratio of 20 are sufficient to obtain representative primary particle emission samples from stationary combustion sources.     

An enhanced PM2.5 air quality forecast model based on nonlinear regression and back-trajectory concentrations

An enhanced PM_2.5 air quality forecast model based on nonlinear regression (NLR) and back-trajectory concentrations has been developed for use in the Louisville, Kentucky metropolitan area. The PM_2.5 air quality forecast model is designed for use in the warm season, from May through September, when PM_2.5 air quality is more likely to be critical for human health. The enhanced PM_2.5 model consists of a basic NLR model, developed for use with an automated air quality forecast system, and an additional parameter based on upwind PM_2.5 concentration, called PM24. The PM24 parameter is designed to be determined manually, by synthesizing backward air trajectory and regional air quality information to compute 24-h back-trajectory concentrations. The PM24 parameter may be used by air quality forecasters to adjust the forecast provided by the automated forecast system. In this study of the 2007 and 2008 forecast seasons, the enhanced model performed well using forecasted meteorological data and PM24 as input. The enhanced PM_2.5 model was compared with three alternative models, including the basic NLR model, the basic NLR model with a persistence parameter added, and the NLR model with persistence and PM24. The two models that included PM24 were of comparable accuracy. The two models incorporating back-trajectory concentrations had lower mean absolute errors and higher rates of detecting unhealthy PM2.5 concentrations compared to the other models.     

Stochastic model to forecast ground-level ozone concentration at urban and rural areas

Stochastic models that estimate the ground-level ozone concentrations in air at an urban and rural sampling points in South-eastern Spain have been developed. Studies of temporal series of data, spectral analyses of temporal series and ARIMA models have been used. The ARIMA model (1,0,0) x (1,0,1)24 satisfactorily predicts hourly ozone concentrations in the urban area. The ARIMA (2,1,1) x (0,1,1)24 has been developed for the rural area. In both sampling points, predictions of hourly ozone concentrations agree reasonably well with measured values. However, the prediction of hourly ozone concentrations in the rural point appears to be better than that of the urban point. The performance of ARIMA models suggests that this kind of modelling can be suitable for ozone concentrations forecasting.     

Measurement of Ultrafine Particle Size Distributions from Coal-, Oil-, and Gas-Fired Stationary Combustion Sources

Abstract

Currently, we have limited knowledge of the physical and chemical properties of emitted primary combustion aerosols and the changes in those properties caused by nucleation, condensation growth of volatile species, and particle coagulations under dilution and cooling in the ambient air. A dilution chamber was deployed to sample exhaust from a pilot-scale furnace burning various fuels at a nominal heat input rate of 160 kW/h^?1 and 3% excess oxygen. The formation mechanisms of particles smaller than 420 nm in electrical mobility diameter were experimentally investigated by measurement with a Scanning Mobility Particle Sizer (SMPS) as a function of aging times, dilution air ratios, combustion exhaust temperatures, and fuel types. Particle formation in the dilution process is a complex mixture of nucleation, coagulation, and condensational growth, depending on the concentrations of available condensable species and solid or liquid particles (such as soot, ash) in combustion exhausts. The measured particle size distributions in number concentrations measured show peaks of particle number concentrations for medium sulfur bituminous coal, No. 6 fuel oil, and natural gas at 40-50 nm, 70-100 nm, and 15-25 nm, respectively. For No. 6 fuel oil and coal, the particle number concentration is constant in the range of a dilution air ratio of 50, but the number decreases as the dilution air ratio decreases to 10. However, for natural gas, the particle number concentration is higher at a dilution air ratio of 10 and decreases at dilution air ratios of 20-50. At a dilution air ratio of 10, severe particle coagulation occurs in a relatively short time. Samples taken at different combustion exhaust temperatures for these fuel types show higher particle number concentrations at 645 K than at 450 K. As the aging time of particles increases, the particles increase in size and the number concentrations decrease. The largest gradient of particle number distribution occurs within the first 10 sec after dilution but shows only minor differences between 10 and 80 sec. The lifetimes of the ultrafine particles are relatively short, with a scale on the order of a few seconds. Results from this study suggest that an aging time of 10 sec and a dilution air ratio of 20 are sufficient to obtain representative primary particle emission samples from stationary combustion sources.     

Measurement and three-dimensional modeling of airflow and pollutant dispersion in an undersea traffic tunnel

Airflow and pollutant dispersion in a cross-harbor traffic tunnel were experimentally and numerically studied. Concentrations of the gaseous pollutants CO, NOx, and total hydrocarbons (THC) at three axial locations in the tunnel, together with traffic flow rate, traffic speed, and types of vehicle were measured. Three-dimensional (3D) turbulent flow and dispersion of air pollutants in the tunnel were modeled and solved numerically using the finite volume method. Traffic emissions were modeled accordingly as banded line sources along the tunnel floor. The results reveal that cross-sectional concentrations are nonuniformly distributed and that concentrations rise with downstream distance. The piston effect of vehicles alone can provide 9-23% dilution of air pollutants in the tunnel, compounded to a 23-74% dilution effect according to the ventilation condition.     

扬水曝气器的水质改善功能及提水、充氧性能研究

扬水曝气器是水源水质改善设备,应用于湖泊水库水源地,抑制藻类生长,控制底泥污染物释放,取得了良好的效果.建立了扬水曝气器上升流速数学模型,用于模拟计算扬水曝气器的提水能力.建立了扬水曝气器曝气室的充氧能力数学模型.在实验室实测了扬水曝气器上升流速及其对水体的充氧过程,实测值与模拟计算值吻合较好,验证了提水、充氧能力数学模型.     

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.     

Evaluation of land-use regression models used to predict air quality concentrations in an urban area

Cohort studies designed to estimate human health effects of exposures to urban pollutants require accurate determination of ambient concentrations in order to minimize exposure misclassification errors. However, it is often difficult to collect concentration information at each study subject location. In the absence of complete subject-specific measurements, land-use regression (LUR) models have frequently been used for estimating individual levels of exposures to ambient air pollution. The LUR models, however, have several limitations mainly dealing with extensive monitoring data needs and challenges involved in their broader applicability to other locations. In contrast, air quality models can provide high-resolution source–concentration linkages for multiple pollutants, but require detailed emissions and meteorological information. In this study, first we predicted air quality concentrations of PM_2.5, NO_x, and benzene in New Haven, CT using hybrid modeling techniques based on CMAQ and AERMOD model results. Next, we used these values as pseudo-observations to develop and evaluate the different LUR models built using alternative numbers of (training) sites (ranging from 25 to 285 locations out of the total 318 receptors). We then evaluated the fitted LUR models using various approaches, including: 1) internal “Leave-One-Out-Cross-Validation” (LOOCV) procedure within the “training” sites selected; and 2) “Hold-Out” evaluation procedure, where we set aside 33–293 tests sites as independent datasets for external model evaluation. LUR models appeared to perform well in the training datasets. However, when these LUR models were tested against independent hold out (test) datasets, their performance diminished considerably. Our results confirm the challenges facing the LUR community in attempting to fit empirical response surfaces to spatially- and temporally-varying pollution levels using LUR techniques that are site dependent. These results also illustrate the potential benefits of enhancing basic LUR models by utilizing air quality modeling tools or concepts in order to improve their reliability or transferability.     

Measurement and Three-Dimensional Modeling of Airflow and Pollutant Dispersion in an Undersea Traffic Tunnel

Abstract

Airflow and pollutant dispersion in a cross-harbor traffic tunnel were experimentally and numerically studied. Concentrations of the gaseous pollutants CO, NO_x, and total hydrocarbons (THC) at three axial locations in the tunnel, together with traffic flow rate, traffic speed, and types of vehicle were measured. Three-dimensional (3D) turbulent flow and dispersion of air pollutants in the tunnel were modeled and solved numerically using the finite volume method. Traffic emissions were modeled accordingly as banded line sources along the tunnel floor. The results reveal that cross-sectional concentrations are nonuniformly distributed and that concentrations rise with downstream distance. The piston effect of vehicles alone can provide 9–23% dilution of air pollutants in the tunnel, compounded to a 23–74% dilution effect according to the ventilation condition.     

Assessing the spatial distribution of nitrogen dioxide in London,Ontario

Land use regression (LUR) models have been widely used to characterize the spatial distribution of urban air pollution and estimate exposure in epidemiologic studies. However, spatial patterns of air pollution vary greatly between cities due to local source type and distribution. London, Ontario, Canada, is a medium-sized city with relatively few and isolated industrial point sources, which allowed the study to focus on the contribution of different transportation sectors to urban air pollution. This study used LUR models to estimate the spatial distribution of nitrogen dioxide (NO₂) and to identify local sources influencing NO₂ concentrations in London, ON. Passive air sampling was conducted at 50 locations throughout London over a 2-week period in May–June 2010. NO₂ concentrations at the monitored locations ranged from 2.8 to 8.9 ppb, with a median of 5.2 ppb. Industrial land use, dwelling density, distance to highway, traffic density, and length of railways were significant predictors of NO₂ concentrations in the final LUR model, which explained 78% of NO₂ variability in London. Traffic and dwelling density explained most of the variation in NO₂ concentrations, which is consistent with LUR models developed in other Canadian cities. We also observed the importance of local characteristics. Specifically, 17% of the variation was explained by distance to highways, which included the impacts of heavily traveled corridors transecting the southern periphery of the city. Two large railway yards and railway lines throughout central areas of the city explained 9% of NO₂ variability. These results confirm the importance of traditional LUR variables and highlight the importance of including a broader array of local sources in LUR modeling. Finally, future analyses will use the model developed in this study to investigate the association between ambient air pollution and cardiovascular disease outcomes, including plaque burden, cholesterol, and hypertension.

Implications: Monitoring and modeling of NO₂ throughout the city of London represents an important step toward assessing air pollution health effects in a mid-sized Canadian city. The study supports the introduction of railways to LUR modeling of NO₂. Railways explained approximately 9% of the variability in ambient NO₂ concentrations in London, which suggests that local sources captured by land-use indicators may contribute to the efficacy of LUR models. These findings provide insights relevant to other medium and smaller sized cities with similar land use and transportation infrastructure. Furthermore, London is a central hub for medical research and treatment in southwestern Ontario, with facilities such as the Robarts Research Institute, London Regional Cancer Program (LRCP), and Stroke Prevention & Atherosclerosis Research Centre (SPARC). The models developed in this study will provide estimates of exposure for future analyses examining air pollution health effects in this data-rich population.     

Source apportionment of indoor air pollution

An understanding of the relative contributions from important pollutant sources to human exposures is necessary for the design and implementation of effective control strategies. In the past, societal efforts to control air pollution have focused almost exclusively on the outdoor (ambient) environment. As a result, substantial amounts of time and money have been spent to limit airborne discharges from mobile and stationary sources. Yet it is now recognized that exposures to elevated pollutant concentrations often occur as a result of indoor, rather than outdoor, emissions. While the major indoor sources have been identified, their relative impacts on indoor air quality have not been well defined. Application of existing source apportionment models to nonindustrial indoor environments is only just beginning. It is possible that these models might be used to distinguish between indoor and outdoor emissions, as well as to distinguish among indoor sources themselves. However, before the feasibility and suitability of source-apportionment methods for indoor applications can be assessed adequately, it is necessary to take account of model assumptions and associated data requirements. This paper examines the issue of indoor source apportionment and reviews the need for emission characterization studies to support such source-apportionment efforts.     

developing a practical dispersion model for an air quality region

A series of computer models have been developed to predict air quality in the New York/New Jersey/Connecticut Air Quality Region. Efforts have been directed at models which have a shorter time scale than climatological models, and which are capable of providing better recommendations for effective abatement and planning, but use input data presently available.

The basic dispersion model for these investigations is a steady-state,nondivergent Gaussian-type model. A modified inventory of SO₂ sources,based on published data for the New York/New Jersey/Connecticut Air Quality Region, was prepared for use with the model. The basic model has been subjected to various internal sensitivity analyses, in which was isolated the variation produced in the pollutant concentration by a given change in each of the factors that contribute, e.g., wind speed, wind direction,mixing depth, stability conditions, source strengths, and grid size for the area sources.

To date, validation tests of the model have been made against the July and August 1969 data for the ten telemetering stations of the New York City Aerometric Network. Hourly as well as averaged concentrations were considered. Various sets of meteorological data from the network stations and the three area airports, were compared and tested. Additional tests, particularly for the winter season, are needed to substantiate the preliminary conclusions suggested by the results to date.

Considerable insight into the relative importance of model components has been acquired from the sensitivity studies. Furthermore the validation results lend support to the belief that a reasonably simple, practical dispersion model can be developed for the region.     

Sequential box models for indoor air quality: Application to airliner cabin air quality

In this paper we present the development and application of a model for indoor air quality. The model represents a departure from the standard box models typically used for indoor environments which has applicability in residences and office buildings. The model has been developed for a physical system consisting of sequential compartments which communicate only with adjacent compartments. Each compartment may contain various source and sink terms for a pollutant as well as leakage, and air transfer from adjacent compartments. The mathematical derivation affords rapid calculation of equilibrium concentrations in an essentially unlimited number of compartments. The model has been applied to air quality in the passenger cabin of three commercial aircraft. Simulations have been performed for environmental tobacco smoke (ETS) under two scenarios, CO₂ and water vapor. Additionally, concentrations in one aircraft have been simulated under conditions different from the standard configuration. Results of the simulations suggest the potential for elevated concentrations of ETS in smoking sections of non-air-recirculating aircraft and throughout the aircraft when air is recirculated. Concentrations of CO₂ and water vapor are consistent with expected results. We conclude that this model may be a useful tool in understanding indoor air quality in general and on aircraft in particular.     

Confidence limits for air quality model evaluations,as estimated by bootstrap and jackknife resampling methods

Air quality models are used to make decisions regarding the construction of industrial plants, the types of fuel that will be burnt and the types of pollution control devices that will be used. It is important to know the uncertainties that are associated with these model predictions. Standard analytical methods found in elementary statistics textbooks for estimating uncertainties are generally not applicable since the distributions of performance measures related to air quality concentrations are not easily transformed to a Gaussian shape. This paper suggests several possible resampling procedures that can be used to calculate uncertainties or confidence limits on air quality model performance. In these resampling methods, many new data sets are drawn from the original data set using an empirical set of rules. A few alternate forms of the socalled bootstrap and jackknife resampling procedures are tested using a concocted data set with a Gaussian parent distributions, with the result that the jackknife is the most efficient procedure to apply, although its confidence bounds are slightly overestimated. The resampling procedures are then applied to predictions by seven air quality models for the Carpinteria coastal dispersion experiment. Confidence intervals on the fractional mean bias and the normalized mean square error are calculated for each model and for differences between models. It is concluded that these uncertainties are sometimes so large for data sets consisting of about 20 elements that it cannot be stated with 95% confidence that the performance measure for the ‘best’ model is significantly different from that for another model.     

Receptor modeling application framework for particle source apportionment

Receptor models infer contributions from particulate matter (PM) source types using multivariate measurements of particle chemical and physical properties. Receptor models complement source models that estimate concentrations from emissions inventories and transport meteorology. Enrichment factor, chemical mass balance, multiple linear regression, eigenvector. edge detection, neural network, aerosol evolution, and aerosol equilibrium models have all been used to solve particulate air quality problems, and more than 500 citations of their theory and application document these uses. While elements, ions, and carbons were often used to apportion TSP, PM10, and PM2.5 among many source types, many of these components have been reduced in source emissions such that more complex measurements of carbon fractions, specific organic compounds, single particle characteristics, and isotopic abundances now need to be measured in source and receptor samples. Compliance monitoring networks are not usually designed to obtain data for the observables, locations, and time periods that allow receptor models to be applied. Measurements from existing networks can be used to form conceptual models that allow the needed monitoring network to be optimized. The framework for using receptor models to solve air quality problems consists of: (1) formulating a conceptual model; (2) identifying potential sources; (3) characterizing source emissions; (4) obtaining and analyzing ambient PM samples for major components and source markers; (5) confirming source types with multivariate receptor models; (6) quantifying source contributions with the chemical mass balance; (7) estimating profile changes and the limiting precursor gases for secondary aerosols; and (8) reconciling receptor modeling results with source models, emissions inventories, and receptor data analyses.