Rapidly locating and characterizing pollutant releases in buildings

Releases of airborne contaminants in or near a building can lead to significant human exposures unless prompt response measures are taken. However, possible responses can include conflicting strategies, such as shutting the ventilation system off versus running it in a purge mode or having occupants evacuate versus sheltering in place. The proper choice depends in part on knowing the source locations, the amounts released, and the likely future dispersion routes of the pollutants. We present an approach that estimates this information in real time. It applies Bayesian statistics to interpret measurements of airborne pollutant concentrations from multiple sensors placed in the building and computes best estimates and uncertainties of the release conditions. The algorithm is fast, capable of continuously updating the estimates as measurements stream in from sensors. We demonstrate the approach using a hypothetical pollutant release in a five-room building. Unknowns to the interpretation algorithm include location, duration, and strength of the source, and some building and weather conditions. Two sensor sampling plans and three levels of data quality are examined. Data interpretation in all examples is rapid; however, locating and characterizing the source with high probability depends on the amount and quality of data and the sampling plan.     

APCA Honors Six Individuals and Three Sections In 1987 Awards Program

Abstract

A growing interest in security and occupant exposure to contaminants revealed a need for fast and reliable identification of contaminant sources during incidental situations. To determine potential contaminant source positions in outdoor environments, current state-of-the-art modeling methods use computational ?uid dynamic simulations on parallel processors. In indoor environments, current tools match accidental contaminant distributions with cases from precomputed databases of possible concentration distributions. These methods require intensive computations in pre- and postprocessing. On the other hand, neural networks emerged as a tool for rapid concentration forecasting of outdoor environmental contaminants such as nitrogen oxides or sulfur dioxide. All of these modeling methods depend on the type of sensors used for real-time measurements of contaminant concentrations. A review of the existing sensor technologies revealed that no perfect sensor exists, but intensity of work in this area provides promising results in the near future. The main goal of the presented research study was to extend neural network modeling from the outdoor to the indoor identification of source positions, making this technology applicable to building indoor environments. The developed neural network Locator of Contaminant Sources was also used to optimize number and allocation of contaminant concentration sensors for real-time prediction of indoor contaminant source positions. Such prediction should take place within seconds after receiving real-time contaminant concentration sensor data. For the purpose of neural network training, a multizone program provided distributions of contaminant concentrations for known source positions throughout a test building. Trained networks had an output indicating contaminant source positions based on measured concentrations in different building zones. A validation case based on a real building layout and experimental data demonstrated the ability of this method to identify contaminant source positions. Future research intentions are focused on integration with real sensor networks and model improvements for much more complicated contamination scenarios.     

Rapidly Locating and Characterizing Pollutant Releases in Buildings

Abstract

Releases of airborne contaminants in or near a building can lead to significant human exposures unless prompt response measures are taken. However, possible responses can include conflicting strategies, such as shutting the ventilation system off versus running it in a purge mode or having occupants evacuate versus sheltering in place. The proper choice depends in part on knowing the source locations, the amounts released, and the likely future dispersion routes of the pollutants. We present an approach that estimates this information in real time. It applies Bayesian statistics to interpret measurements of airborne pollutant concentrations from multiple sensors placed in the building and computes best estimates and uncertainties of the release conditions. The algorithm is fast, capable of continuously updating the estimates as measurements stream in from sensors. We demonstrate the approach using a hypothetical pollutant release in a five-room building. Unknowns to the interpretation algorithm include location, duration, and strength of the source, and some building and weather conditions. Two sensor sampling plans and three levels of data quality are examined. Data interpretation in all examples is rapid; however, locating and characterizing the source with high probability depends on the amount and quality of data and the sampling plan.     

Assessing sensitivity of source term estimation

Source term estimation algorithms compute unknown atmospheric transport and dispersion modeling variables from concentration observations made by sensors in the field. Insufficient spatial and temporal resolution in the meteorological data as well as inherent uncertainty in the wind field data make source term estimation and the prediction of subsequent transport and dispersion extremely difficult. This work addresses the question: how many sensors are necessary in order to successfully estimate the source term and meteorological variables required for atmospheric transport and dispersion modeling?The source term estimation system presented here uses a robust optimization technique – a genetic algorithm (GA) – to find the combination of source location, source height, source strength, surface wind direction, surface wind speed, and time of release that produces a concentration field that best matches the sensor observations. The approach is validated using the Gaussian puff as the dispersion model in identical twin numerical experiments. The limits of the system are tested by incorporating additive and multiplicative noise into the synthetic data. The minimum requirements for data quantity and quality are determined by an extensive grid sensitivity analysis. Finally, a metric is developed for quantifying the minimum number of sensors necessary to accurately estimate the source term and to obtain the relevant wind information.     

State of the Art Report on Mathematical Methods for Groundwater Pollution Source Identification

The reliable assessment of hazards or risks arising from groundwater contamination problems and the design of efficient and effective techniques to mitigate these problems require the capability to predict the behavior of chemical contaminants in flowing water. Most attempts at quantifying contaminant transport have relied on a solution of some form of a well-known governing equation referred to as advection-dispersion-reaction equation. To choose an appropriate remediation strategy, knowledge of the contaminant release source and time release history becomes pertinent. As additional contaminated sites are being detected, it is almost impossible to perform exhaustive drilling, testing, and chemical fingerprint analysis every time. Moreover, chemical fingerprinting and site records are not sufficient to allow a unique solution for the timing of source releases. The purpose of this paper is to present and review mathematical methods that have been developed during the past 15 years to identify the contaminant source location and recover the time release history.     

State of the Art Report on Mathematical Methods for Groundwater Pollution Source Identification

The reliable assessment of hazards or risks arising from groundwater contamination problems and the design of efficient and effective techniques to mitigate these problems require the capability to predict the behavior of chemical contaminants in flowing water. Most attempts at quantifying contaminant transport have relied on a solution of some form of a well-known governing equation referred to as advection-dispersion-reaction equation. To choose an appropriate remediation strategy, knowledge of the contaminant release source and time release history becomes pertinent. As additional contaminated sites are being detected, it is almost impossible to perform exhaustive drilling, testing, and chemical fingerprint analysis every time. Moreover, chemical fingerprinting and site records are not sufficient to allow a unique solution for the timing of source releases. The purpose of this paper is to present and review mathematical methods that have been developed during the past 15 years to identify the contaminant source location and recover the time release history.     

Evaluating the performance of regional-scale photochemical modeling systems: Part II—ozone predictions

In this paper, the concept of scale analysis is applied to evaluate ozone predictions from two regional-scale air quality models. To this end, seasonal time series of observations and predictions from the RAMS3b/UAM-V and MM5/MAQSIP (SMRAQ) modeling systems for ozone were spectrally decomposed into fluctuations operating on the intra-day, diurnal, synoptic and longer-term time scales. Traditional model evaluation statistics are also presented to illustrate how the scale analysis approach can help improve our understanding of the models’ performance. The results indicate that UAM-V underestimates the total variance (energy) of the ozone time series when compared with observations, but shows a higher mean value than the observations. On the other hand, MAQSIP is able to better reproduce the average energy and mean concentration of the observations. However, both modeling systems do not capture the amount of variability present on the intra-day time scale primarily due to the grid resolution used in the models. For both modeling systems, the correlations between the predictions and observations are insignificant for the intra-day component, high for the diurnal component because of the inherent diurnal cycle but low for the amplitude of the diurnal component, and highest for the synoptic and baseline components. This better model performance on longer time scales suggests that current regional-scale models are most skillful in characterizing average patterns over extended periods, rather than in predicting concentrations at specific locations, during 1–2 day episodic events. In addition, we discuss the implications of these results to using the model-predicted daily maximum ozone concentrations in the regulatory framework in light of the uncertainties introduced by the models’ poor performance on the intra-day and diurnal time scales.     

New methodology to investigate potential contaminant mass fluxes at the stream-aquifer interface by combining integral pumping tests and streambed temperatures

The spatial pattern and magnitude of mass fluxes at the stream-aquifer interface have important implications for the fate and transport of contaminants in river basins. Integral pumping tests were performed to quantify average concentrations of chlorinated benzenes in an unconfined aquifer partially penetrated by a stream. Four pumping wells were operated simultaneously for a time period of 5 days and sampled for contaminant concentrations. Streambed temperatures were mapped at multiple depths along a 60m long stream reach to identify the spatial patterns of groundwater discharge and to quantify water fluxes at the stream-aquifer interface. The combined interpretation of the results showed average potential contaminant mass fluxes from the aquifer to the stream of 272microgm(-2)d(-1) MCB and 71microgm(-2)d(-1) DCB, respectively. This methodology combines a large-scale assessment of aquifer contamination with a high-resolution survey of groundwater discharge zones to estimate contaminant mass fluxes between aquifer and stream.     

Development of a fuzzy-stochastic nonlinear model to incorporate aleatoric and epistemic uncertainty

Site uncertainties significantly influence groundwater flow and contaminant transport predictions. Aleatoric and epistemic uncertainty are both identified in site characterization and represented using proper uncertainty theories. When one theory best represents one parameter whereas a different theory may be more suitable for another parameter, the hybrid propagation of aleatoric (random) and epistemic (nonrandom) uncertainties will occur. The computational challenges of joint propagation of aleatoric and epistemic uncertainty through groundwater flow and contaminant transport models are significant. A fuzzy-stochastic nonlinear model was developed in this paper to incorporate these two types of uncertain site information and reduce the computational cost. The results show that (1) the computational cost using the nonlinear model is reduced compared with that of using the sparse grid algorithm and Monte Carlo methods; (2) the uncertainty of hydraulic conductivity (K) significantly influences the water head and solute distribution at the observation wells compared to other uncertain parameters, such as the storage coefficient and the distribution coefficient (K_d); and (3) the combination of multiple uncertain parameters substantially affects the simulation results. Neglecting site uncertainties may lead to unrealistic predictions.     

Contaminant exposure in terrestrial vertebrates

Here we review mechanisms and factors influencing contaminant exposure among terrestrial vertebrate wildlife. There exists a complex mixture of biotic and abiotic factors that dictate potential for contaminant exposure among terrestrial and semi-terrestrial vertebrates. Chemical fate and transport in the environment determine contaminant bioaccessibility. Species-specific natural history characteristics and behavioral traits then play significant roles in the likelihood that exposure pathways, from source to receptor, are complete. Detailed knowledge of natural history traits of receptors considered in conjunction with the knowledge of contaminant behavior and distribution on a site are critical when assessing and quantifying exposure. We review limitations in our understanding of elements of exposure and the unique aspects of exposure associated with terrestrial and semi-terrestrial taxa. We provide insight on taxa-specific traits that contribute, or limit exposure to, transport phenomenon that influence exposure throughout terrestrial systems, novel contaminants, bioavailability, exposure data analysis, and uncertainty associated with exposure in wildlife risk assessments. Lastly, we identify areas related to exposure among terrestrial and semi-terrestrial organisms that warrant additional research.     

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.     

Simulation of carrier-facilitated transport of phenanthrene in a layered soil profile

The appropriate prediction of the fate of the contaminant is an essential step when evaluating the risk of severe groundwater pollutions-in particular in the context of natural attenuation. We numerically study the reactive transport of phenanthrene at the field scale in a multilayer soil profile based on experimental data. The effect of carrier facilitation by dissolved organic carbon is emphasized and incorporated in the model. Previously published simulations are restricted to the saturated zone and/or to homogeneous soil columns at the laboratory scale. A numerical flow and transport model is extended and applied to understand and quantify the relevant processes in the case of a strongly sorbing hydrophobic organic compound that is subject to carrier facilitation in the unsaturated zone. The contaminant migration is investigated on long- and short-term time scales and compared to predictions without carrier facilitation. The simulations demonstrate the importance of carrier facilitation and suggest strongly to take this aspect into account. By carrier facilitation breakthrough times at the groundwater level decreased from 500 to approximately 8 years and concentration peaks increased by two orders of magnitude in the long-term simulation assuming a temporary spill in an initially unpolluted soil with a non-sorbing carrier.     

A laboratory study of sediment and contaminant release during gas ebullition

Significant quantities of gas are generated from labile organic matter in contaminated sediments. The implications for the gas generation and subsequent release of contaminants from sediments are unknown but may include enhanced direct transport such as pore water advection and diffusion. The behavior of gas in sediments and the resulting migration of a polyaromatic hydrocarbon, viz phenanthrene, were investigated in an experimental system with methane injection at the base of a sediment column. Hexane above the overlying water layer was used to trap any phenanthrene migrating out of the sediment layer. The rate of suspension of solid particulate matter from the sediment bed into the overlying water layer was also monitored. The experiments indicated that significant amounts of both solid particulate matter and contaminant can be released from a sediment bed by gas movement with the amount of release related to the volume of gas released. The effective mass transfer coefficient of gas bubble-facilitated contaminant release was estimated under field conditions, being around three orders of magnitude smaller than that of bioturbation. A thin sand-capping layer (2 cm) was found to dramatically reduce the amount of contaminant or particles released with the gas because it could prevent or at least reduce sediment suspension. Based on the experimental observations, gas bubble-facilitated contaminant transport pathways for both uncapped and capped systems were proposed. Sediment cores were sliced to obtain phenanthrene concentration. X-ray computed tomography (CT) was used to investigate the void space distribution in the sediment penetrated by gas bubbles. The results showed that gas bubble migration could redistribute the sediment void spaces and may facilitate pore water circulation in the sediment.     

Transport of reactive colloids and contaminants in groundwater: effect of nonlinear kinetic interactions

Transport of reactive colloids in groundwater may enhance the transport of contaminants in groundwater. Often, the interpretation of results of transport experiments is not a simple task as both reactions of colloids with the solid matrix and reactions of contaminants with the solid matrix and mobile and immobile colloids may be time dependent and nonlinear. Further colloid transport properties may differ from solute transport properties. In this paper, a one-dimensional model for coupled and contaminant in a porous medium (COLTRAP) is presented together with simulation results. Calculated breakthrough curves (BTC's) during contamination and decontamination show systematically the effect of nonlinear and kinetic interactions on contaminant transport in the presence of reactive colloids, and the effect of colloid transport properties that differ from solute transport properties. It is shown that in case of linear kinetic reactions, the rate of exchange of mobile and immobile colloids have a large impact on the shape of BTC's even if the solid matrix is saturated with respect to colloids. BTC's during the contamination and decontamination phase have identical shapes in this case. Moreover, the slow reactions of contaminants and colloids may lead to unretarded breakthrough of contaminants. Independent of reaction rates, nonlinear reactions lead to BTC's that are steeper during contamination than in the linear case. A characteristic aspect of nonlinear sorption is that shapes of BTC's differ during the contamination and decontamination phase. It has been observed that shapes of some of the simulated adsorption and desorption curves are similar as shapes found in experiments reported in literature. This stresses the importance of incorporating both kinetics and nonlinearity in models for coupled colloid and contaminant transport and the capability of COLTRAP to interpret experimental results. Finally, to figure out whether nonlinear processes play a role, it is very important to consider both contamination and decontamination in transport experiments.     

Assessing impacts of partial mass depletion in DNAPL source zones: II. Coupling source strength functions to plume evolution

Analytical solutions, describing the time-dependent DNAPL source-zone mass and contaminant discharge rate, derived previously in Part I [Falta, R.W., Rao, P.S., Basu, N., this issue. Assessing the impacts of partial mass depletion in DNAPL source zones: I. Analytical modeling of source strength functions and plume response. J. Contam. Hydrol.] are used as a flux-boundary condition in a semi-analytical contaminant transport model. These analytical solutions assume a power relationship between the flow-averaged source concentration, and the source DNAPL mass; the empirical exponent (gamma) is a function of the flow field heterogeneity, DNAPL architecture, and the correlation between them. The DNAPL source strength terms can account for partial source remediation, either at time zero, or at some later time after the DNAPL release. The transport model considers advection, retardation, three-dimensional dispersion, and sequential first-order decay/production of several species. A separate solution is used to compute the time-dependent mass of each contaminant in the plume. A series of examples using different values of gamma shows how the benefits of partial DNAPL source remediation can vary with site conditions. In general, when gamma>1, relatively large short-term reductions in the plume concentrations and mass occur, but the source longevity is not strongly affected. Conversely, when gamma<1, the short-term reductions in the plume concentrations and mass are smaller, but the source longevity can be greatly reduced. In either case, the source remediation effort is much more effective if it is undertaken at an early time, before much contaminant mass has entered the plume. If the remediation effort is significantly delayed, the leading parts of the plume are not affected by the source remediation, and additional control or remediation of the plume itself is required.     

Analysis of groundwater contamination using concentration-time series recorded during an integral pumping test: bias introduced by strong concentration gradients within the plume

When only few monitoring wells are available to assess the extent and level of groundwater contamination, inversion of concentration breakthrough curves acquired during an integral pumping test can be used as an alternative quantification method. The idea is to use concentration-time series recorded during integral pumping tests through an inversion technique to estimate contaminant mass fluxes crossing a control plane. In this paper, we examine how a longitudinal concentration gradient along a contaminant plume length scale affects the estimated inversed-concentration distribution and its associated mass flux. The analytically inversed-concentration distribution at the imaginary control plane (ICP) is compared to a numerically generated concentration distribution, treating the latter one as a "real contaminant plume" characterized by the presence of a longitudinal concentration gradient. It is found that the analytically inversed-concentration can lead to overestimation or underestimation of concentration distribution values depending on the transport time period and dispersivity values. At lower dispersivity values, with shorter transport time periods, the analytically inversed-concentration distribution overestimates the "real" concentration distribution. A better fit of the estimated concentration distribution to the "real" one is observed when the transport time period increases, i.e. when the advective front has already crossed the ICP. However, for higher dispersivity values, underestimation of the real concentration distribution is observed. Deviation of the inversed-concentration distribution from the "real" one is assessed for a site-specific concentration gradient term. A concentration gradient adjusted contaminant mass flux is thus formulated to evaluate groundwater contamination levels at a given time period through an ICP. This concentration gradient ratio can indicate whether the ICP is well positioned to evaluate accurately contaminant mass fluxes which are representative of groundwater contamination levels.     

Analysis of groundwater contamination using concentration–time series recorded during an integral pumping test: Bias introduced by strong concentration gradients within the plume

When only few monitoring wells are available to assess the extent and level of groundwater contamination, inversion of concentration breakthrough curves acquired during an integral pumping test can be used as an alternative quantification method. The idea is to use concentration–time series recorded during integral pumping tests through an inversion technique to estimate contaminant mass fluxes crossing a control plane. In this paper, we examine how a longitudinal concentration gradient along a contaminant plume length scale affects the estimated inversed-concentration distribution and its associated mass flux. The analytically inversed-concentration distribution at the imaginary control plane (ICP) is compared to a numerically generated concentration distribution, treating the latter one as a “real contaminant plume” characterized by the presence of a longitudinal concentration gradient. It is found that the analytically inversed-concentration can lead to overestimation or underestimation of concentration distribution values depending on the transport time period and dispersivity values. At lower dispersivity values, with shorter transport time periods, the analytically inversed-concentration distribution overestimates the “real” concentration distribution.A better fit of the estimated concentration distribution to the “real” one is observed when the transport time period increases, i.e. when the advective front has already crossed the ICP. However, for higher dispersivity values, underestimation of the real concentration distribution is observed. Deviation of the inversed-concentration distribution from the “real” one is assessed for a site-specific concentration gradient term. A concentration gradient adjusted contaminant mass flux is thus formulated to evaluate groundwater contamination levels at a given time period through an ICP. This concentration gradient ratio can indicate whether the ICP is well positioned to evaluate accurately contaminant mass fluxes which are representative of groundwater contamination levels.     

Field-scale effective matrix diffusion coefficient for fractured rock: results from literature survey

Matrix diffusion is an important mechanism for solute transport in fractured rock. We recently conducted a literature survey on the effective matrix diffusion coefficient, D_m^e, a key parameter for describing matrix diffusion processes at the field scale. Forty field tracer tests at 15 fractured geologic sites were surveyed and selected for the study, based on data availability and quality. Field-scale D_m^e values were calculated, either directly using data reported in the literature, or by reanalyzing the corresponding field tracer tests. The reanalysis was conducted for the selected tracer tests using analytic or semi-analytic solutions for tracer transport in linear, radial, or interwell flow fields. Surveyed data show that the scale factor of the effective matrix diffusion coefficient (defined as the ratio of D_m^e to the lab-scale matrix diffusion coefficient, D_m, of the same tracer) is generally larger than one, indicating that the effective matrix diffusion coefficient in the field is comparatively larger than the matrix diffusion coefficient at the rock-core scale. This larger value can be attributed to the many mass-transfer processes at different scales in naturally heterogeneous, fractured rock systems.Furthermore, we observed a moderate, on average trend toward systematic increase in the scale factor with observation scale. This trend suggests that the effective matrix diffusion coefficient is likely to be statistically scale-dependent. The scale-factor value ranges from 0.5 to 884 for observation scales from 5 to 2000 m. At a given scale, the scale factor varies by two orders of magnitude, reflecting the influence of differing degrees of fractured rock heterogeneity at different geologic sites. In addition, the surveyed data indicate that field-scale longitudinal dispersivity generally increases with observation scale, which is consistent with previous studies. The scale-dependent field-scale matrix diffusion coefficient (and dispersivity) may have significant implications for assessing long-term, large-scale radionuclide and contaminant transport events in fractured rock, both for nuclear waste disposal and contaminant remediation.     

A comparison of numerical solutions of partial differential equations with probabilistic and possibilistic parameters for the quantification of uncertainty in subsurface solute transport

Traditionally, uncertainty in parameters are represented as probabilistic distributions and incorporated into groundwater flow and contaminant transport models. With the advent of newer uncertainty theories, it is now understood that stochastic methods cannot properly represent non random uncertainties. In the groundwater flow and contaminant transport equations, uncertainty in some parameters may be random, whereas those of others may be non random. The objective of this paper is to develop a fuzzy-stochastic partial differential equation (FSPDE) model to simulate conditions where both random and non random uncertainties are involved in groundwater flow and solute transport. Three potential solution techniques namely, (a) transforming a probability distribution to a possibility distribution (Method I) then a FSPDE becomes a fuzzy partial differential equation (FPDE), (b) transforming a possibility distribution to a probability distribution (Method II) and then a FSPDE becomes a stochastic partial differential equation (SPDE), and (c) the combination of Monte Carlo methods and FPDE solution techniques (Method III) are proposed and compared. The effects of these three methods on the predictive results are investigated by using two case studies. The results show that the predictions obtained from Method II is a specific case of that got from Method I. When an exact probabilistic result is needed, Method II is suggested. As the loss or gain of information during a probability–possibility (or vice versa) transformation cannot be quantified, their influences on the predictive results is not known. Thus, Method III should probably be preferred for risk assessments.     

Mathematical Model for Pollution Source Identification in Rivers

Release of pollution into rivers is required to be handled with special consideration to environmental standards. For this purpose, it is essential to specify the contribution of each pollution source in contamination of water resources. In this study, a mathematical model is proposed for determining locations and concentration release histories of polluting point sources using measured downstream river concentrations via an inverse problem framework. The inverse solution is based on the integral equation obtained from applying the Green's function method on the one-dimensional advection-dispersion contaminant transport equation. Discretization of this integral equation results in a linear, over-determined and ill-posed system of algebraic equations that are solved by using the Tikhonov regularization method. Several examples and some real field data are investigated to illustrate the abilities of the proposed model. Results imply that the proposed method is effective and can identify the pollution sources in rivers with acceptable accuracy.