An Air Quality Data Analysis System for Interrelating Effects,Standards, and Needed Source Reductions: Part 4. A Three-Parameter Averaging-Time Model

Urban air pollutant concentration data often tend to fit a two-parameter averaging-time model having three characteristics: (1) pollutant concentrations are (two-parameter) lognormally distributed for all averaging times; (2) median concentrations are proportional to averaging time raised to an exponent; and (3) maximum concentrations are approximately inversely proportional to averaging time raised to an exponent. Concentration data obtained near many isolated point sources and in some urban areas often do not fit a two-parameter lognormal distribution. An increment (either positive or negative) can be added to each such concentration in order to fit the data instead to a three-parameter lognormal distribution. This increment has been incorporated as the third parameter in a new three-parameter averaging-time model that can be used in both point-source and urban settings. Examples show how this new model can be used to analyze SO₂ concentration data obtained near a point source to determine the degree of emission reduction needed to achieve the national ambient air quality standards.     

Estimating Emissions from Air Concentration Measurements

A one-year-long experiment in which two different tracers were simultaneously released from two different locations was used to test various hybrid receptor modeling techniques to estimate the tracer emissions using the measured air concentrations and a meteorological model. Air concentrations were measured over an 8-hour averaging time at three sites 14 to 40 km downwind. When the model was used to estimate emissions at only one tracer source, 6 percent of the short-term (8-h) emission estimates were within a factor of 2 of the actual emissions. Temporal averaging of the 8-h data enhanced the precision of the estimate such that after 10 days 42 percent of the estimates were within a factor of 2 and after six months all of them (each source-receptor pair) were within a factor of 2. To test the ability of the model to separate two sources, both tracer sources were combined, and a multiple linear regression technique was used to determine the emissions from each source from a time series of air concentration measurements representing the sum of both tracers. In general, 50 percent of the short-term estimates were within a factor of 10, 25 percent were biased low, and in another 25 percent the regression technique failed. The bias and failures are attributed to low or no correlation between measured air concentrations and model calculated dispersion factors. In the regression method increased temporal averaging did not consistently improve the emission estimate since the ability of the model to distinguish emissions between sources was diminished with increased averaging time. However, including progressively longer time periods (more data) into the regression or spatially averaging the data over all the receptors was found to be the most effective method to improve the estimated emissions. At best about 75 percent of the estimated monthly emission data were within a factor of 10 of the measured values. This suggests that the usefulness of meteorological models and statistical methods to address questions of source attribution requires many data points to reduce the uncertainty in the emission estimates.     

Probability density function of non-reactive solute concentration in heterogeneous porous formations

Available models of solute transport in heterogeneous formations lack in providing complete characterization of the predicted concentration. This is a serious drawback especially in risk analysis where confidence intervals and probability of exceeding threshold values are required. Our contribution to fill this gap of knowledge is a probability distribution model for the local concentration of conservative tracers migrating in heterogeneous aquifers. Our model accounts for dilution, mechanical mixing within the sampling volume and spreading due to formation heterogeneity. It is developed by modeling local concentration dynamics with an Ito Stochastic Differential Equation (SDE) that under the hypothesis of statistical stationarity leads to the Beta probability distribution function (pdf) for the solute concentration. This model shows large flexibility in capturing the smoothing effect of the sampling volume and the associated reduction of the probability of exceeding large concentrations. Furthermore, it is fully characterized by the first two moments of the solute concentration, and these are the same pieces of information required for standard geostatistical techniques employing Normal or Log-Normal distributions. Additionally, we show that in the absence of pore-scale dispersion and for point concentrations the pdf model converges to the binary distribution of [Dagan, G., 1982. Stochastic modeling of groundwater flow by unconditional and conditional probabilities, 2, The solute transport. Water Resour. Res. 18 (4), 835-848.], while it approaches the Normal distribution for sampling volumes much larger than the characteristic scale of the aquifer heterogeneity. Furthermore, we demonstrate that the same model with the spatial moments replacing the statistical moments can be applied to estimate the proportion of the plume volume where solute concentrations are above or below critical thresholds. Application of this model to point and vertically averaged bromide concentrations from the first Cape Cod tracer test and to a set of numerical simulations confirms the above findings and for the first time it shows the superiority of the Beta model to both Normal and Log-Normal models in interpreting field data. Furthermore, we show that assuming a-priori that local concentrations are normally or log-normally distributed may result in a severe underestimate of the probability of exceeding large concentrations.     

Appropriateness of selecting different averaging times for modelling chronic and acute exposure to environmental odours

Odour emissions are episodic, characterised by periods of high emission rates, interspersed with periods of low emissions. It is frequently the short term, high concentration peaks that result in annoyance in the surrounding population. Dispersion modelling is accepted as a useful tool for odour impact assessment, and two approaches can be adopted. The first approach of modelling the hourly average concentration can underestimate total odour concentration peaks, resulting in annoyance and complaints. The second modelling approach involves the use of short averaging times. This study assesses the appropriateness of using different averaging times to model the dispersion of odour from a landfill site. We also examine perception of odour in the community in conjunction with the modelled odour dispersal, by using community monitors to record incidents of odour. The results show that with the shorter averaging times, the modelled pattern of dispersal reflects the pattern of observed odour incidents recorded in the community monitoring database, with the modelled odour dispersing further in a north easterly direction. Therefore, the current regulatory method of dispersion modelling, using hourly averaging times, is less successful at capturing peak concentrations, and does not capture the pattern of odour emission as indicated by the community monitoring database. The use of short averaging times is therefore of greater value in predicting the likely nuisance impact of an odour source and in framing appropriate regulatory controls.     

Calculating mean concentrations for steady sources in recirculating wakes by a particle trajectory method

A discrete vortex model of the recirculating flow behind a two-dimensional backward-facing step is used to calculate the trajectories of particles released from a fixed point. By averaging over a large number of such trajectories, an estimate is made of the mean concentration profile associated with a steady source in the wake. These estimates are verified against experimental data for point- and line-sources. The importance of incorporating a ‘random walk’ in calculating the trajectories is demonstrated. The mean flow in the discrete vortex model used appears to be the most critical factor in determining mean concentrations. The poorest predictions appear to be associated with the longest trajectories.Particle ‘recirculation times’ are also briefly examined and it is shown how these are related to the ‘residence times’ of Vincent (1976, Atmospheric Environment11, 765–774) and others. It is suggested that such residence times may not be an appropriate means of quantifying near-wake dispersion if sources are inside the wake. Advantages of a ‘particle trajectory’ method, as against a diffusion equation method, for dealing with dispersion in inhomogeneous flow are finally presented.     

Estimating Long-Term Ground Level Concentrations of SO2 from Short-Term Peak Data

Maximum concentrations of sulfur dioxide vary approximately inversely as the square root of the averaging time for periods up to a year, a much longer time period than those reported in previously published studies. This finding is based on analyses of SO₂ and tracer gas concentrations measured in the vicinities of a test stack, a petrochemical complex, and eight power plants. Therefore, for a given set of emission conditions, long-term concentrations of SO₂ and other conservative pollutants emitted by well-defined sources can be rapidly estimated from maximum short-term concentrations, and vice versa. The scatter in the data corresponds to an estimation error of about a factor of three for averaging time conversions of one day to one year. This is approximately the same error associated with conventional calculations. Therefore, the use of averaging time conversions is justified as a rapid screening technique to estimate compliance with ambient air quality standards.     

On the vertical diffusion from a ground level source in a developing mixing layer

The quasi-stationary condition of vertical diffusion in a mixing layer is discussed using dimensional analysis, and some numerical calculations are carried out to evaluate the concentration in a non-stationary state. General relationships of vertical diffusion from a ground level source are suggested using several scale times of the mixing layer and it is shown that the quasi-stationary condition of diffusion can be satisfied when the travel time and averaging time of the diffusing matter are smaller than the time scale of the development of the mixing layer.Calculated results show that in a non-stationary state, the normalized concentration and spread of a plume using convective layer scaling are not essentially different from those in a stationary state. However, the real concentration in a non-stationary state increases with the increase in averaging time, due to the effect of development in the mixing layer.     

Demolition of high-rise public housing increases particulate matter air pollution in communities of high-risk asthmatics

Public housing developments across the United States are being demolished, potentially increasing local concentrations of particulate matter (PM) in communities with high burdens of severe asthma. Little is known about the impact of demolition on local air quality. At three public housing developments in Chicago, IL, PM with an aerodynamic diameter < 10 microm (PM10) and < 2.5 microm were measured before and during high-rise demolition. Additionally, size-selective sampling and real-time monitoring were concurrently performed upwind and downwind of one demolition site. The concentration of particulates attributable to demolition was estimated after accounting for background urban air pollution. Particle microscopy was performed on a small number of samples. Substantial increases of PM10 occurred during demolition, with the magnitude of that increase varying based on sampler distance, wind direction, and averaging time. During structural demolition, local concentrations of PM10 42 m downwind of a demolition site increased 4- to 9-fold above upwind concentrations (6-hr averaging time). After adjusting for background PM10, the presence of dusty conditions was associated with a 74% increase in PM10 100 m downwind of demolition sites (24-hr averaging times). During structural demolition, short-term peaks in real-time PM10 (30-sec averaging time) occasionally exceeded 500 microg/m(3). The median particle size downwind of a demolition site (17.3 microm) was significantly larger than background (3 microm). Specific activities are associated with realtime particulate measures. Microscopy did not identify asbestos or high concentrations of mold spores. In conclusion, individuals living near sites of public housing demolition are at risk for exposure to high particulate concentrations. This increase is characterized by relatively large particles and high short-term peaks in PM concentration.     

An Air Quality Data Analysis System for Interrelating Effects,Standards, and Needed Source Reductions: Part 3. Vegetation Injury

Acute leaf injury data are analyzed for 19 plant species exposed to ozone or sulfur dioxide. The data can be depicted by a new leaf injury mathematical model with two characteristics: (1) a constant percentage of leaf surface is injured by an air pollutant concentration that is inversely proportional to exposure duration raised to an exponent; (2) for a given exposure duration, the percent leaf injury as a function of pollutant concentration tends to fit a lognormal frequency distribution. Leaf injury as a function of laboratory exposure duration is modeled and compared with ambient air pollutant concentration measurements for various averaging times to determine which exposure durations are probably most important for setting ambient air quality standards to prevent or reduce visible leaf injury. The 8 hour average appears to be most important for most of the plants investigated for most sites, 1 hr concentrations are important for most plants at a few sites, and 3 hr S0₂ concentrations are important for some plants, especially those exposed to isolated point sources of the pollutant. The 1, 3, and 8 hr threshold injury concentrations are listed for each of the 19 plant species studied. To prevent or reduce acute leaf injury, fixed, nonoverlapping ambient air quality measurements and standards are recommended for averaging times of 1, 3, and 8hr.     

Air Quality Model Evaluation and Uncertainty

The many advances made in air quality model evaluation procedures during the past ten years are discussed and some components of model uncertainty presented. Simplified statistical procedures for operational model evaluation are suggested. The fundamental model performance measures are the mean bias, the mean square error, and the correlation. The bootstrap resampling technique is used to estimate confidence limits on the performance measures, In order to determine if a model agrees satisfactorily with data or if one model is significantly different from another model. Applications to two tracer experiments are described.

It is emphasized that review and evaluation of the scientific components of models are often of greater Importance than the strictly statistical evaluation. A necessary condition for acceptance Of a model should be that it is scientifically correct. It Is shown that even in research-grade tracer experiments, data Input errors can cause errors In hourly-average model predictions of point concentrations almost as large as the predictions themselves. The turbulent or stochastic component of model uncertainty has a similar magnitude. These components of the uncertainty decrease as averaging time increases.     

Demolition of High-Rise Public Housing Increases Particulate Matter Air Pollution in Communities of High-Risk Asthmatics

Abstract

Public housing developments across the United States are being demolished, potentially increasing local concentrations of particulate matter (PM) in communities with high burdens of severe asthma. Little is known about the impact of demolition on local air quality. At three public housing developments in Chicago, IL, PM with an aerodynamic diameter <10 μm (PM₁₀) and <2.5 μm were measured before and during high-rise demolition. Additionally, size-selective sampling and real-time monitoring were concurrently performed upwind and downwind of one demolition site. The concentration of particulates attributable to demolition was estimated after accounting for background urban air pollution. Particle microscopy was performed on a small number of samples. Substantial increases of PM₁₀ occurred during demolition, with the magnitude of that increase varying based on sampler distance, wind direction, and averaging time. During structural demolition, local concentrations of PM₁₀ 42 m downwind of a demolition site increased 4- to 9-fold above upwind concentrations (6-hr averaging time). After adjusting for background PM₁₀, the presence of dusty conditions was associated with a 74% increase in PM₁₀ 100 m downwind of demolition sites (24-hr averaging times). During structural demolition, short-term peaks in real-time PM₁₀ (30-sec averaging time) occasionally exceeded 500 μg/m³. The median particle size downwind of a demolition site (17.3 μm) was significantly larger than background (3 μm). Specific activities are associated with real-time particulate measures. Microscopy did not identify asbestos or high concentrations of mold spores. In conclusion, individuals living near sites of public housing demolition are at risk for exposure to high particulate concentrations. This increase is characterized by relatively large particles and high short-term peaks in PM concentration.     

A Review of Techniques Available for Estimating Short-Term NO2 concentrations

The Environmental Protection Agency is reviewing the need for a short-term NO₂ standard based on an averaging time of three hours or less. State Implementation plans and New Source Reviews will require air quality simulation techniques capable of estimating ambient NO₂ concentrations. There is a need for multi-source (urban) models and for point source models.

A review of currently available techniques for the estimation of NO₂ concentrations resulting from NO_x point sources is presented. The available methods include simple screening techniques and refined reactive plume models. The screening techniques first use a standard gaussian dispersion model to estimate the maximum 1 hr NO_x concentration caused by the source. The second step involves estimating the fraction of this NO* concentration occurring as NO₂.

Reactive plume models numerically simulate the simultaneous effects of dispersion and chemistry on NO₂ concentrations. Organic as well as inorganic reactions are incorporated. Reactive plume models should be used, where screening techniques indicate the potential for violation of the NO₂ standard.

Current generation reactive plume models neglect the effect of turbulent concentration fluctuation on NO₂ formation and use inappropriately large dispersion coefficients to estimate plume concentrations. Approaches being developed to resolve these problems are discussed.     

Errors in Measurement and Counting of Particles Using Light Scattering

More efficient air sampling programs can be designed, and clearer interpretations of their data made, if important theoretical aspects are clearly understood. The choice of a sampling time is an important decision affecting the results. Empirical and theoretical calculations show that the averaging effect of sampling time attenuates responses to short period fluctuations in pollutant concentrations. Data for sulfur dioxide concentrations in six cities are examined.

The body acts as a sampling mechanism also, and concentrations inside the body fluctuate less than those outside. These damping processes are quantitatively described. A significant biological parameter is the product of the biological half-life of a pollutant and the fraction of entrance to total resistance for its passage through the body. When sampling time is four times this parameter, attenuation of significant fluctuations is about the same in both samples and the body; when it is twice the parameter, the “sampling window” transmits all significant fluctuations better than the “biological window.” Shorter sampling periods appear to give unnecessary fine detail for biological application according to this theoretical model.     

Urban CO Concentrations and Vehicle Emissions

The effect of the general growth of CO vehicular emissions in urban areas on the CAMP station measurements in downtown areas, where vehicular traffic is saturated is considered. With the assumption that the street-level CO concentration is derived from the sum of an urban background term and a local street-effect term, the urban background CO concentration is computed with a diffusion model by introducing a simple area source distribution. The local street-effect term is taken to be constant at a saturation emission level corresponding to a saturation traffic density when the emission per vehicle-mile and meteorological conditions are fixed. The present analysis indicates that the local street-effect term, AC, has a major role in determining street-level concentrations for pollutants, such as CO, whose air quality standard is based on maximum concentrations with averaging times of 1 hour and 8 hours. The relevance of this analysis to the abatement requirements of the Clean Air Amendments and to the driving cycle adopted is discussed.     

Indoor Ozone Exposures

Indoor and outdoor ozone concentrations were measured from late May through October at three office buildings with very different ventilation rates. The indoor values closely tracked the outdoor values, and, depending on the ventilation rate, were 20 to 80 percent of those outdoors. The Indoor/outdoor data are adequately described with a mass balance model. The model can also be coupled with reported air exchange rates to estimate indoor/outdoor ratios for other structures. The results from this and previous studies indicate that Indoor concentrations are frequently a significant fraction of outdoor values. These observations, and the fact that most people spend greater than 90 percent of their time indoors, indicate that indoor ozone exposure (concentration × time) is greater than outdoor exposure for many people. Relatively Inexpensive strategies exist to reduce indoor ozone levels, and these could be implemented to reduce the public’s total ozone exposure.     

Personal carbon monoxide exposures of preschool children in Helsinki, Finland—comparison to ambient air concentrations

The associations of personal carbon monoxide (CO) exposures with ambient air CO concentrations measured at fixed monitoring sites, were studied among 194 children aged 3–6 yr in four downtown and four suburban day-care centers in Helsinki, Finland. Each child carried a personal CO exposure monitor between 1 and 4 times for a time period of between 20 and 24 h. CO concentrations at two fixed monitoring sites were measured simultaneously. The CO concentrations measured at the fixed monitoring sites were usually lower (mean maximum 8-h concentration: 0.9 and 2.6 mg m⁻³) than the personal CO exposure concentrations (mean maximum 8-h concentration: 3.3 mg m⁻³). The fixed site CO concentrations were poor predictors of the personal CO exposure concentrations. However, the correlations between the personal CO exposure and the fixed monitoring site CO concentrations increased (−0.03–−0.12 to 0.13–0.16) with increasing averaging times from 1 to 8 h. Also, the fixed monitoring site CO concentrations explained the mean daily or weekly personal CO exposures of a group of simultaneously measured children better than individual exposure CO concentrations. This study suggests that the short-term CO personal exposure of children cannot be meaningfully assessed using fixed monitoring sites.     

A hybrid modeling approach to resolve pollutant concentrations in an urban area

A modeling tool that can resolve contributions from individual sources to the urban environment is critical for air-toxics exposure assessments. Air toxics are often chemically reactive and may have background concentrations originated from distant sources. Grid models are the best-suited tools to handle the regional features of these chemicals. However, these models are not designed to resolve pollutant concentrations on local scales. Moreover, for many species of interest, having reaction time scales that are longer than the travel time across an urban area, chemical reactions can be ignored in describing local dispersion from strong individual sources making Lagrangian and plume-dispersion models practical. In this study, we test the feasibility of developing an urban hybrid simulation system. In this combination, the Community Multi-scale Air Quality model (CMAQ) provides the regional background concentrations and urban-scale photochemistry, and local models such as Hybrid Single Particle Lagrangian Integrated Trajectory model (HYSPLIT) and AMS/EPA Regulatory Model (AERMOD) provide the more spatially resolved concentrations due to local emission sources. In the initial application, the HYSPLIT, AERMOD, and CMAQ models are used in combination to calculate high-resolution benzene concentrations in the Houston area. The study period is from 18 August to 4 September of 2000. The Mesoscale Model 5 (MM5) is used to create meteorological fields with a horizontal resolution of 1×1 km². In another variation to this approach, multiple HYSPLIT simulations are used to create a concentration ensemble to estimate the contribution to the concentration variability from point sources. HYSPLIT simulations are used to model two sources of concentration variability; one due to variability created by different particle trajectory pathways in the turbulent atmosphere and the other due to different flow regimes that might be introduced when using gridded data to represent meteorological data fields. The ensemble mean concentrations determined by HYSPLIT plus the concentrations estimated by AERMOD are added to the CMAQ calculated background to estimate the total mean benzene concentration. These estimated hourly mean concentrations are also compared with available field measurements.     

A model for non-specific toxicity with aquatic organisms over relatively long periods of exposure time

Experimental data have shown that the internal lethal concentrations of halobenzenes for aquatic organisms decreased with exposure time. In this paper, a model based on the concept of life expectancy reduction was developed to describe this relationship. The model was verified with experimental data for fish (Gambusia affinis) and juvenile crab (Porturius pelagicus(L)). It is proposed that long term non-specific toxicity can be measured as the reduction of the life expectancy of the exposed organism per unit internal concentration (or volume fraction) of the toxic compound. The model can be used to estimate internal lethal concentration at any given exposure period and vice versa. The model can also be used to estimate chronic values of the internal concentration, of the toxicants. It provides a useful tool for assessment of environmental risk of organic compounds in aquatic ecosystems.