Pesticide dispersion by spraying under tropical conditions

Pesticide air pollution by spraying was evaluated under different temperature, humidity and wind climatic conditions in Brazil. Field experiments were performed with application towards the soil and in guava orchards, where spray dispersion was monitored by adding p-aminobenzoic acid (PABA), a fluorescent substance, as a tracer to the water contained in the spray tanks. Samples were collected with filter membranes (Whatman 180025), and the PABA was extracted from the filters by shaking with water in a Petri dish and measured in a spectrofluorometer. A spray aimed towards the soil with filters positioned on the ground and hung at different heights did not show different upward dispersion as observed when lateral pulverization was conducted. In this case, a tractor with a sprayer moved through a 3 m high and 6 m wide frame with filter membranes mounted at 60 cm intervals. Spray dispersion patterns were modified by guava leaf resistance. No influence of temperature and humidity was observed in this short-lived spraying process. Nevertheless, wind drift can occur during airborne dispersion and is an important pesticide pollution source which requires control. Droplets with PABA powered by assisted spraying upwards returned to the ground by gravity and, therefore, did not constitute a vertical source of atmospheric pollution.     

A methodology to estimate odors around landfill sites: the use of methane as an odor index and its utility in landfill siting

The estimation of odor production and dispersion from landfill sites is a very complicated task because of the different chemical species that exist in biogas. To site a new landfill, it is necessary to know the distance that odors can travel around the landfill under atmospheric conditions that increase the concentration of pollutants. Although CH4 is an odorless gas, it can be used as an index to determine the dispersion of low-reactivity odorous species around a landfill site. Methane production rates, estimated by biogas production models, were used by an air dispersion model to determine the spatial distribution of CH4 around landfill sites. By utilizing dispersion models under extreme atmospheric conditions, a maximum CH4 concentration around the landfills was determined. Based on the ratio between CH4 and odorous chemical species, the spatial distribution of the concentration of an odorous species was determined for those species with low reactivity in the atmosphere. For odorous species with high reactivity in the atmosphere, a dispersion-reaction model must be used. In this way, the acceptable distance between new landfills and residential areas can be determined. The proposed methodology could be used as a design tool for those who are interested in landfill siting.     

PCDD/F enviromental impact from municipal solid waste bio-drying plant

The present work indentifies some environmental and health impacts of a municipal solid waste bio-drying plant taking into account the PCDD/F release into the atmosphere, its concentration at ground level and its deposition. Four scenarios are presented for the process air treatment and management: biofilter or regenerative thermal oxidation treatment, at two different heights. A Gaussian dispersion model, AERMOD, was used in order to model the dispersion and deposition of the PCDD/F emissions into the atmosphere. Considerations on health risk, from different exposure pathways are presented using an original approach. The case of biofilter at ground level resulted the most critical, depending on the low dispersion of the pollutants. Suggestions on technical solutions for the optimization of the impact are presented.     

Electrostatic Precipitator Performance from the Point of View of Gas Treatment Time

This paper describes a risk assessment methodology for preliminary assessment of municipal sludge incineration. The methodology is a valuable tool in that it can be used for determining the hazard indices of chemical contaminants that might be present in sewage sludge used in incineration. The paper examines source characteristics (i.e., facility design), atmospheric dispersion of emission, and resulting human exposure and risk from sludge incinerators. Seven of the ten organics were screened for furtherinvestigation. An example of the calculations are presented for cadmium.     

Bayesian inference for source determination with applications to a complex urban environment

The problem of determining the source of an emission from the limited information provided by a finite and noisy set of concentration measurements obtained from real-time sensors is an ill-posed inverse problem. In general, this problem cannot be solved uniquely without additional information. A Bayesian probabilistic inferential framework, which provides a natural means for incorporating both errors (model and observational) and prior (additional) information about the source, is presented. Here, Bayesian inference is applied to find the posterior probability density function of the source parameters (location and strength) given a set of concentration measurements. It is shown how the source–receptor relationship required in the determination of the likelihood function can be efficiently calculated using the adjoint of the transport equation for the scalar concentration. The posterior distribution of the source parameters is sampled using a Markov chain Monte Carlo method. The inverse source determination method is validated against real data sets acquired in a highly disturbed flow field in an urban environment. The data sets used to validate the proposed methodology include a water-channel simulation of the near-field dispersion of contaminant plumes in a large array of building-like obstacles (Mock Urban Setting Trial) and a full-scale field experiment (Joint Urban 2003) in Oklahoma City. These two examples demonstrate the utility of the proposed approach for inverse source determination.     

83rd Annual Meeting & Exhibition

Abstract

The estimation of odor production and dispersion from landfill sites is a very complicated task because of the different chemical species that exist in biogas. To site a new landfill, it is necessary to know the distance that odors can travel around the landfill under atmospheric conditions that increase the concentration of pollutants. Although CH₄ is an odorless gas, it can be used as an index to determine the dispersion of low-reactivity odorous species around a landfill site. Methane production rates, estimated by biogas production models, were used by an air dispersion model to determine the spatial distribution of CH₄ around landfill sites. By utilizing dispersion models under extreme atmospheric conditions, a maximum CH₄ concentration around the landfills was determined. Based on the ratio between CH₄ and odorous chemical species, the spatial distribution of the concentration of an odorous species was determined for those species with low reactivity in the atmosphere. For odorous species with high reactivity in the atmosphere, a dispersion-reaction model must be used. In this way, the acceptable distance between new landfills and residential areas can be determined. The proposed methodology could be used as a design tool for those who are interested in landfill siting.     

A Test Reference Year (TRY) approach to gas dispersion in the atmosphere

For environmental analysis such as the dispersion of pollutants in the atmosphere, it is essential to have meteorological data that are relevant for a long period. In this paper, we explore the possibility of using an environmental Test Reference Year (TRY), i.e., a set of real, contemporaneous and hourly meteorological variables, 'extracted' from a hourly series of at least 10 years, for modelling pollutant dispersion in the atmosphere. The classical approach, based on a statistical data set, implies the loss of important information such as the real correlation between the different meteorological variables, and this implies crude approximation in the simulation results. We compare the simulation results with the long hourly 10 years data set (which can be considered a 'brute force' approach, since it requires a huge amount of data and time processing, but it is here considered the most severe benchmark) and with the statistical data set commonly used. It is shown that the results obtained using the TRY have a good agreement with the ones obtained with the simulation of the 10 years and they are also much better than those obtained using the statistical data set.     

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.     

Performance Evaluation of Integral and Analytical Plume Rise Algorithms

The purpose of this study was to evaluate the performance of current regulatory algorithms for predicting plume rise for refinerytype sources (short stacks and a wide range of source conditions) and the performance of new or alternate algorithms which may provide better estimates. To meet the objectives, five plume rise algorithms were statistically evaluated against ten field and laboratory plume rise data bases. Two forms of the Briggs plume rise equations were tested because they are almost exclusively used in current EPA regulatory models. Two modified Briggs equations were tested to assess how simple modifications can Improve the accuracy of the estimates. The fifth algorithm was a numerical solution to the basic equations for conservation of mass, momentum, and energy often referred to as an Integral plume rise algorithm. This algorithm was selected because It handles the wide range of source and atmospheric boundary-layer conditions that affect trajectories of plumes from refinery stacks.

Ten independent plume rise data bases were assembled that covered a wide range of source and meteorological conditions. From the data bases, a total of 107 different data sets were obtained and each data set included plume rise observations versus downwind distance for one source and meteorological condition. Each model was run for each data set and the root-mean-square and mean error between model and observation was computed for use in statistically evaluating model performance.

The statistical evaluation of the algorithms showed that the rms error (considering all data bases) for the Integral plume rise algorithm was approximately 30 percent less than the errors for all other algorithms tested. This difference was significant at the 95 percent confidence level. The results suggest that improved plume rise estimates in regulatory models applied to refineries and other appropriate sources could be achieved to reduce costs and improve ambient air quality estimates through the use of an integral plume rise algorithm.     

A large-scale experiment on mass transfer of trichloroethylene from the unsaturated zone of a sandy aquifer to its interfaces

A large-scale experiment was conducted to investigate the transport of trichloroethylene (TCE) vapors in the unsaturated zone and to determine the mass transfer to the groundwater and the atmosphere. The experiment involved injection of 5 1 of TCE in the unsaturated zone under controlled conditions, with multidepth sampling of gas and water through the unsaturated zone and across the capillary zone into underlying groundwater. The mass transfer of TCE vapors from the vadose zone to the atmosphere was quantified using a vertical flux chamber. A special soil water sampler was used to monitor transport across the capillary fringe. Experimental data indicated that TCE in the unsaturated zone was mainly transported to the atmosphere and this exchange reduced significantly the potential for groundwater pollution. The maximum measured TCE flux to the atmosphere was about 3 g/m(2)/day. Observed and calculated fluxes based on vertical TCE vapor concentration gradients and Fick's law were in good agreement. This confirms that TCE vapor transport under the experimental conditions was governed essentially by molecular diffusion. TCE vapors also caused a lower, but significant contamination of the underlying groundwater by dispersion across the capillary fringe with a corresponding maximum flux of about 0.1 g/m(2)/day. This mass transfer to groundwater is partly uncertain due to an inadvertent entry of some nonaqueous phase liquid (NAPL) from the source area into the saturated zone. Application of an analytical solution to estimate the TCE flux from the unsaturated zone to the groundwater indicated that this phenomenon is not only influenced by molecular diffusion but also by vertical dispersion. The mass balance indicates that, under the given experimental conditions (e.g. proximity of the source emplacement relative to the soil surface, relatively high permeable porous medium), nearly 95% of the initial TCE mass was transferred to the atmosphere.     

The Fifth International Clean Air Congress

ABSTRACT

The main objective of this study was to investigate the capabilities of the receptor-oriented inverse mode Lagrangian Stochastic Particle Dispersion Model (LSPDM) with the 12-km resolution Mesoscale Model 5 (MM5) wind field input for the assessment of source identification from seven regions impacting two receptors located in the eastern United States. The LSPDM analysis was compared with a standard version of the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) single-particle backward-trajectory analysis using inputs from MM5 and the Eta Data Assimilation System (EDAS) with horizontal grid resolutions of 12 and 80 km, respectively. The analysis included four 7-day summertime events in 2002; residence times in the modeling domain were computed from the inverse LSPDM runs and HYPSLIT-simulated backward trajectories started from receptor-source heights of 100, 500, 1000, 1500, and 3000 m. Statistics were derived using normalized values of LSPDM- and HYSPLIT-predicted residence times versus Community Multiscale Air Quality model-predicted sulfate concentrations used as baseline information. From 40 cases considered, the LSPDM identified first- and second-ranked emission region influences in 37 cases, whereas HYSPLIT-MM5 (HYSPLIT-EDAS) identified the sources in 21 (16) cases. The LSPDM produced a higher overall correlation coefficient (0.89) compared with HYSPLIT (0.55–0.62). The improvement of using the LSPDM is also seen in the overall normalized root mean square error values of 0.17 for LSPDM compared with 0.30–0.32 for HYSPLIT. The HYSPLIT backward trajectories generally tend to underestimate near-receptor sources because of a lack of stochastic dispersion of the backward trajectories and to overestimate distant sources because of a lack of treatment of dispersion. Additionally, the HYSPLIT backward trajectories showed a lack of consistency in the results obtained from different single vertical levels for starting the backward trajectories. To alleviate problems due to selection of a backward-trajectory starting level within a large complex set of 3-dimensional winds, turbulence, and dispersion, results were averaged from all heights, which yielded uniform improvement against all individual cases.

IMPLICATIONS Backward-trajectory analysis is one of the standard procedures for determining the spatial locations of possible emission sources affecting given receptors, and it is frequently used to enhance receptor modeling results. This analysis simplifies some of the relevant processes such as pollutant dispersion, and additional methods have been used to improve receptor-source relationships. A methodology of inverse Lagrangian stochastic particle dispersion modeling was used in this study to complement and improve standard backward-trajectory analysis. The results show that inverse dispersion modeling can identify regional sources of haze in national parks and other regions of interest.     

Simulation of the evolution of particle size distributions containing coarse particulate in the atmospheric surface layer with a simple convection-diffusion-sedimentation model

The Fugitive Dust Model (FDM) and Industrial Source Complex (ISC), widely used coarse particulate dispersion models, have been shown inaccurate due to the neglect of vertical variations in atmospheric wind speed and turbulent diffusivity (Vesovic et al., 2001), omission of the gravitational advection velocity, and an underestimation of the ground deposition velocity (Kim and Larson, 2001). A simple, transient two-dimensional convection-diffusion-sedimentation model is proposed to simulate the evolution in particle size distribution of an aerosol ‘puff’ containing coarse particulate in the atmospheric surface layer. Monin-Okhubov similarity theory, accompanied by empirical observations made by Businger et al. (1971), is adopted to characterize the surface layer wind speed and turbulent diffusivity profiles over a wide range of atmospheric conditions. A first order analysis of the crossing trajectories effect suggests simulation data presented here are not significantly affected by particle inertia. The model is validated against Suffield experimental data in which coarse particulate deposition was measured out to a distance of 800 m from the source (Walker, 1965). Good agreement is found for the decay in ground deposits with distance from the source for stable atmospheres. Deposition data was also simulated for unstable atmospheric stratification and the current model was determined to modestly underestimate the peak concentration with increasing accuracy further downwind of the release. The current model's effective deposition velocity was compared to that suggested by Kim et al. (2000) and shows improvement with respect to FDM. Lastly, the model was used to simulate the dispersion of nine lognormal aerosol puffs in the lowest 50 m of the atmospheric surface layer for four classes of atmospheric stability. The simulated mass median aerodynamic diameters (MMAD) at multiple downwind sampling locations were calculated and plotted with distance from the source. The first 50 m from the source was found to have a substantial impact on the evolution of MMAD for stable atmospheric conditions. Away from the source, it was observed that particle size distributions were truncated by removal of all particles larger than about 60 μm. A particle Peclet number was also defined to quantify the relative importance of turbulent dispersion and sedimentation on particle motion in the vertical direction.     

Improving pollutant source characterization by better estimating wind direction with a genetic algorithm

In homeland security applications, it is often necessary to characterize the source location and strength of a potentially harmful contaminant. Correct source characterization requires accurate meteorological data such as wind direction. Unfortunately, available meteorological data is often inaccurate or unrepresentative, having insufficient spatial and temporal resolution for precise modeling of pollutant dispersion. To address this issue, a method is presented that simultaneously determines the surface wind direction and the pollutant source characteristics. This method compares monitored receptor data to pollutant dispersion model output and uses a genetic algorithm (GA) to find the combination of source location, source strength, and surface wind direction that best matches the dispersion model output to the receptor data. A GA optimizes variables using principles from genetics and evolution.The approach is validated with an identical twin experiment using synthetic receptor data and a Gaussian plume equation as the dispersion model. Given sufficient receptor data, the GA is able to reproduce the wind direction, source location, and source strength. Additional runs incorporating white noise into the receptor data to simulate real-world variability demonstrate that the GA is still capable of computing the correct solution, as long as the magnitude of the noise does not exceed that of the receptor data.     

Unsteady state dispersion of air pollutant from a time dependent point source forming a secondary pollutant

In this paper, the dispersion of air pollutant from a time dependent point source forming a secondary pollutant has been investigated by taking into account the dry and wet deposition on the ground. The unsteady state three-dimensional diffusion equation has been solved for prescribed forms of time dependent flux at the source. The case when the flux is given by the step function has been applied to study the dispersion of an episode like the MIC leakage in Bhopal, India.     

Application of Unknown Groundwater Pollution Source Release History Estimation Methodology to Distributed Sources Incorporating Surface-Groundwater Interactions

Sources of contamination of groundwater are often difficult to characterize. However, it is essential for effective remediation of polluted groundwater resources. This study demonstrates an application of the linked simulation-optimization based methodology to estimate the release history from spatially distributed sources of pollution at an illustrative abandoned mine-site. In linked simulation-optimization approaches a numerical groundwater flow and transport simulation model is linked to the optimization model. In this study, topographic and geologic characteristics of the abandoned mine-site were simulated using a three-dimensional (3D) numerical groundwater flow model. Transport of contaminant in the groundwater was simulated using a 3D transient advective-dispersive contaminant transport model. Adsorption or chemical reaction of the contaminant was not considered in the contaminant transport model. Adaptive simulated annealing (ASA) was employed for solving the optimization problem. An optimization algorithm generates the candidate solutions corresponding to various unknown groundwater source characteristics. The candidate solutions are used as input in the numerical groundwater transport simulation model to generate the concentration of pollutant in the study area. This information is used to calculate the objective function value, which is utilized by the optimization algorithm to improve the candidate solution. This process continues until an optimal solution is obtained. Optimal solutions obtained in this study show that the linked simulation-optimization based methodology is potentially applicable for the characterization of spatially distributed pollutant sources, typically present at abandoned mine-sites.     

A model study for ambient air quality analysis using routine meteorological observations

In the present study, more realistic and easily adaptable input parameters have been used with a view to investigating the long-range air quality analysis for the dispersion of air pollutants emitted from an area source with a multiple box model. The model formulation has been discussed at length for the ground level sources when convective conditions prevail. The routine meteorological observations have been used for the computation of sensible surface heat flux, friction velocity and mixing depth. A radiation model provides the estimates of the sensible surface heat flux. Based on the similarity theory, an iterative procedure has been adopted for the estimation of friction velocity which provides a coupling of radiation computation and the surface layer of the planetary boundary layer through surface heat flux expression. The important parameters—wind speed and eddy diffusivity profiles—have been derived and have been used to obtain the concentration patterns as hourly averages. The procedure could be easily adopted where observed meteorological parameters may be used for studying the dispersal of pollutants from the ground level sources.     

Evaluation of the reactive and optics model of emissions (ROME)

The reactive and optics model of emissions (ROME) is a reactive plume visibility model that simulates the potential atmospheric impacts of stack emissions. We present here an evaluation of the ability of ROME to simulate several plume physical and chemical variables, using an experimental data base that consists of a total of 40 case studies from four field programs. The evaluation variables include plume height, horizontal width, NO_x and SO₂ maximum concentrations, NO₂/NO_x concentration ratio at the plume centerline, and plume-to-sky radiance ratios. Three algorithms used to simulate plume dispersion in ROME were compared: (1) the empirical Pasquill–Gifford–Turner (PGT) scheme, (2) a first-order closure (FOC) algorithm and (3) a second-order closure (SOC) algorithm that simulates the instantaneous plume dimensions.The plume height results show a correlation of 0.82 between simulated and measured values and a gross error that is 13% of the mean measured value. For plume horizontal dispersion, the second-order closure algorithm produces a moderate correlation (0.54) and a small bias (5% of the mean measured value) in comparison with the field data. Although the PGT scheme also demonstrates moderate correlation with the measurements, it produces a negative bias by significantly underestimating plume horizontal dispersion. The first-order closure algorithm overestimates plume width and shows the least correlation (with the measurements) of the three dispersion algorithms.For the NYSEG data set where coordinated measurements of stack emissions, meteorology at plume height and plume characteristics were available, the SOC algorithm provides better correlations for NO_x concentrations, NO₂/NO_x ratios and plume visibility than the FOC and PGT algorithms. For plume visibility, the SOC algorithm shows a correlation of 0.96 at 405 nm, the wavelength where the plume was visible, and it simulates no visible plume at the other wavelengths (550 and 700 nm).A comparison of ROME simulations with those of the plume visibility model PLUVUE II shows that ROME, with the SOC algorithm, performs better for all variables.     

A Numerical Modeling Technique for Estimating Sulfur Dioxide Dry Deposition Due to Local Source Emissions

This paper describes a methodology for estimating the effect of local source emissions on dry deposition of sulfur dioxide in regions of complex terrain. Airflow in complex terrain is simulated by a time-dependent dynamical model for the meteorological fields. The results of the dynamical model are used to drive a semi-stochastic Lagrangian dispersion model in order to evaluate concentrations resulting from local source emissions. The Lagrangian dispersion model is coupled with a dry deposition treatment which includes the effects of both surface properties and micrometeoroiogical factors on deposition.

A sample application is discussed for a source in the Shenandoah Valley. The largest concentrations and deposition rates were obtained shortly after sunrise, during the transition from the nocturnal to the daytime flow regime. These results suggest that dry deposition may be episodic.     

On the influence of the urban roughness sublayer on turbulence and dispersion

The concept of the urban roughness sublayer is discussed and this lowest atmospheric layer over a rough surface is shown to have a non-negligible vertical extension over typical urban surfaces. The existing knowledge on the turbulence and flow structure within an urban roughness sublayer is reviewed, focusing on the height dependence of turbulent fluxes and a scaling approach for turbulence statistics, such as velocity variances, in the above-roof part of the roughness sublayer. Finally, the implication of this turbulence and flow structure upon dispersion characteristics is investigated. The most prominent difference of explicitly taking into account the roughness sublayer in a dispersion simulation (as compared to assuming a `constant flux layer') is a clearly enhanced ground level concentration far downwind from the source. For the example of a tracer release experiment over a (sub) urban surface (Copenhagen) it is shown that introducing the roughness sublayer clearly improves the model performance.