A Simple Method of Calculating Dispersion from Urban Area Sources

A simple but physically realistic model is shown to be adequate for estimating pollutant concentrations due to area sources in cities. In this model, the surface concentration is directly proportional to the local area source strength and inversely proportional to the wind speed. The model performs nearly as well as much more complex models that require the use of digital computers.     

Modeling Short Range Air Dispersion from Area Sources of Non-buoyant Toxics

A model based on K-theory has been developed for describing the short range air dispersion from area sources of non-buoyant toxics. Model parameter estimation is via boundary layer theory. Lateral dispersion by plume meander is considered but ail other sources of horizontal dispersion are neglected. The model can be applied on and near area sources and it can be adapted for predictions of downwind concentrations with a wide variety of meteorological Inputs.

The model has been evaluated by simulating the data obtained during atmospheric tracer studies and by comparison to vinyl chloride concentrations near the BKK landfill in southern California. The model appears to represent a useful and accurate tool for regulatory planning and risk assessment close to area sources of toxics.     

Adherence of Sulfur Dioxide Concentrations in the Vicinity of a Steam Plant to Plume Dispersion Models

Air monitoring data for a calendar year at one of the TVA power plants has been used to evaluate the appropriateness of the Sutton, the Bosanquet and Pearson, and the USPHS-TVA atmospheric dispersion models to predict ground level concentrations of sulfur dioxide from emission and meterological data. Aerometric data included one half hourly average sulfur dioxide concentrations, recorded by four Thomas autometers, and the necessary meterological parameters for the solving of atmospheric dispersion models. Based on these meterological parameters and observed plume rise data, over 4000 one half hourly average maximum and minimum expected ground line sulfur dioxide concentrations were predicted for each of the above dispersion models by the use of computer techniques. The plant is a line source; however, an empirical correction was applied to emission data to reduce them to emissions for an equivalent point source. The predicted sulfur dioxide levels for each of the dispersion models were compared to the measured levels throughout the year. Three different sets of diffusion coefficients were applied to the Sutton model and successful predictions, according to a criterion utilizing an acceptable range of concentration, varied from 66 to 93%. The Bosanquet and Pearson model produced successful predictions 90% of the time, while the USPHS-TVA model was successful 94% of the time.Unsuccessful predictions were primarily overestimates.     

The Nature and Sources of Haze in the Shenandoah Valley/Blue Ridge Mountains Area

In order to investigate the nature and sources of regional haze, the General Motors mobile Atmospheric Research Laboratory was used in the summer of 1980 to monitor ambient air quality in the Shenandoah Valley of northern Virginia. On the average, 92% of the total light extinction was due to scattering by particles; the remainder was due to scattering by gases and absorption by gases and particles. Sulfate aerosols were the most Important visibility-reducing species. Averaging 55% of the fine participate mass, sulfates (and associated water) accounted for 78% of the total light extinction. The second most abundant fine particulate, accounting for 29% of the fine mass, was carbon—most of which was organic. Most of the remaining particulate mass and extinction were due to crustal materials. It is estimated that 78–86% of the total light extinction was caused by anthropogenic aerosol, most of which originated in major source areas of the midwest.     

Multivariate Stochastic Models of Sulphur Dioxide Pollution in an Urban Area

Three multivariate stochastic mathematical models of daily SO₂ pollution in an urban area (Milan, Italy) during the heating season (mid-October/end of March) are illustrated in the paper. Each model is characterized by a different number of external inputs. Precisely, the first model has no inputs (it is simply an autoregressive relationship), the second one has a temperature input (roughly accounting for emission), the third one has two inputs (temperature and wind speed). From each model a real-time predictor is derived, namely a recursive relationship which, at the end of each day, allows future pollution levels to be forecast on the basis of current concentration and meteorological measurements. The quality of the forecast is rather satisfactory, even in episode situations. The improvements in forecast performance when turning from a predictor with less external inputs to a predictor with more external inputs (i.e., when exploiting more information about meteorology) are also pointed out in the paper.     

Correlating Emissions with Time and Temperature to Predict Worst-Case Emissions from Open Liquid Area Sources

Abstract

The two primary factors influencing ambient air pollutant concentrations are emission rate and dispersion rate. Gaussian dispersion modeling studies for odors, and often other air pollutants, vary dispersion rates using hourly meteorological data. However, emission rates are typically held constant, based on one measured value. Using constant emission rates can be especially inaccurate for open liquid area sources, like wastewater treatment plant units, which have greater emissions during warmer weather, when volatilization and biological activity increase. If emission rates for a wastewater odor study are measured on a cooler day and input directly into a dispersion model as constant values, odor impact will likely be underestimated. Unfortunately, because of project schedules, not all emissions sampling from open liquid area sources can be conducted under worst-case summertime conditions. To address this problem, this paper presents a method of varying emission rates based on temperature and time of the day to predict worst-case emissions. Emissions are varied as a linear function of temperature, according to Henry’s law, and a tenth order polynomial function of time. Equation coefficients are developed for a specific area source using concentration and temperature measurements, captured over a multiday period using a data-logging monitor. As a test case, time/temperature concentration correlation coefficients were estimated from field measurements of hydrogen sulfide (H₂S) at the Rowlett Creek Wastewater Treatment Plant in Garland, TX. The correlations were then used to scale a flux chamber emission rate measurement according to hourly readings of time and temperature, to create an hourly emission rate file for input to the dispersion model ISCST3. ISCST3 was then used to predict hourly atmospheric concentrations of H₂S. With emission rates varying hourly, ISCST3 predicted 384 acres of odor impact, compared with 103 acres for constant emissions. Because field sampling had been conducted on relatively cool days (85–90 °F), the constant emission rate underestimated odor impact significantly (by 73%).     

Sensitivity Analysis and Evaluation of MicroFacCO: A Microscale Motor Vehicle Emission Factor Model for CO Emissions

ABSTRACT

This paper presents a sensitivity analysis of a microscale emission factor model (MicroFacCO) for predicting realtime site-specific motor vehicle CO emissions to input variables, as well as a limited field study evaluation of the model. The sensitivity analysis has shown that MicroFacCO emission estimates are very sensitive to vehicle fleet composition, speed, and ambient temperature. For the present U.S. traffic fleet, the CO emission rate (g/mi) is increased by more than 500% at 5 mph in comparison with a speed greater than 40 mph and by ~67% at ambient temperatures of 45 °F and ≥95 °F in comparison with an ambient temperature of 75 °F.     

Investigation to Determine the Possible Need for a Regulation on Organic Compound Emissions from Stationary Sources in the San Francisco Bay Area

The problem of an investigation of the need for a regulation on organic compound emissions in the San Francisco Bay Area can be divided into two major areas: 1. Is there a need for a regulation on organic compound emissions? What is the extent of photochemical smog effects? How much control is necessary to achieve the desired effects?

2. (2) If the need exists for an organic compound regulation, can a performance type regulation be written for all types of organic compound emissions? Must the regulation be directed toward specific industries or types of emissions? Can the regulation be adequately enforced—both practically and legally?

This paper will describe the studies undertaken by the District Staff to answer the first set of questions. Work covering the second group of questions is now under investigation.     

地下水曝气理论模型研究进展

地下水曝气(Air Sparging,AS)是修复饱和土壤及地下水有机污染的有效技术.AS多相流动过程中气液流动以及污染物传质过程的模型研究是AS技术的关键因素,详细介绍了近年来AS系统的理论模型方法及研究进展,并对其效果进行评价.     

太湖流域重点污染源治理的投资效益比较分析

以１９９８年江苏省太湖流域列入国务院限期达标计划的７７０家重点污染企业为样本,以水污染物为重点,定量评估在行业、地区、规模、所有制结构之间治理投资的显著差异性及其实际指导价值与应用前景。     

Sources and fate of PCDD and PCDF

PCDD and PCDF were found in urban air particulates from St. Louis and Washington, D.C., and in sediments from the Great Lakes and Siskiwit Lake, Isle Royale. The similarity between the PCDD and PCDF found in air particulates and sediment samples and the presence of PCDD and PCDF in sediment from Siskiwit Lake (a location which can receive only atmospheric inputs) suggest that these compounds are emitted to the atmosphere from combustion sources. The historical input of PCDD and PCDF to dated sediment cores shows a strong increase since 1940, and this suggests that the incineration of chlorinated organic compounds is an important source of PCDD and PCDF to the environment.     

Combining Neural Network Models to Predict Spatial Patterns of Airborne Pollutant Accumulation in Soils around an Industrial Point Emission Source

Abstract

Neural networks (NNs) have the ability to model a wide range of complex nonlinearities. A major disadvantage of NNs, however, is their instability, especially under conditions of sparse, noisy, and limited data sets. In this paper, different combining network methods are used to benefit from the existence of local minima and from the instabilities of NNs. A nonlinear k-fold cross-validation method is used to test the performance of the various networks and also to develop and select a set of networks that exhibits a low correlation of errors. The various NN models are applied to estimate the spatial patterns of atmospherically transported and deposited lead (Pb) in soils around an historical industrial air emission point source. It is shown that the resulting ensemble networks consistently give superior predictions compared with the individual networks because, for the ensemble networks, R² values were found to be higher than 0.9 while, for the contributing individual networks, values for R² ranged between 0.35 and 0.85. It is concluded that combining networks can be adopted as an important component in the application of artificial NN techniques in applied air quality studies.