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.     

Estimation of Pollutant Transport and Concentration Distributions over Complex Terrain of Southern California Using Airborne Lidar

Airborne lidar data collected during the South Coast Air Basin/Southeast Desert Oxidant Transport Study in summer 1981 were used to provide estimates of concentration distributions of particles and oxidants, and of pollutant transport in mountain passes and over mountain slopes. The results support the idea that for certain situations, given a few representative in situ measurements for use in calibration, airborne lidar might be used to develop detailed concentration fields. When combined with appropriate measurements of wind speed profiles with height, useful estimates of pollutant transport flux might also be provided.     

The Odor Impact Model

Abstract

This paper presents the simulation and field evaluation results of two approaches to localize pollutant emission sources with open-path Fourier transform infrared (OPFTIR) spectroscopy. The first approach combined the plume’s peak location information reconstructed from the Smooth Basis Function Minimization (SBFM) algorithm and the wind direction data to calculate source projection lines. In the second approach, the plume’s peak location was determined with the Monte Carlo methodology by randomly sampling within the beam segment having the largest path-integrated concentration. We first conducted a series of simulation studies to investigate the sensitivity of using different basis functions in the SBFM algorithm. It was found that fitting with the beta and Weibull basis functions generally gave better estimates of the peak locations than with the normal basis function when the plumes were mainly within the OP-FTIR’s monitoring line. However, for plumes that were symmetric to the peak position or spread over the OP-FTIR, fitting with the normal basis function gave better performance. In the field experiment, two tracer gases were released simultaneously from two locations and the OP-FTIR collected data downwind from the sources with a maximum beam path length of 97 m. For the first approach, the release locations were within the 0.25- to 0.5-probability area only after the uncertainty of the peak locations was included in the calculation process. The second approach was easy to implement and still performed as satisfactorily as the first approach. The distances from the sources to the best-fit lines (i.e., the regression lines) of the estimated locations were smaller than 10 m.     

Developing and evaluating techniques for localizing pollutant emission sources with open-path Fourier transform infrared measurements and wind data

This paper presents the simulation and field evaluation results of two approaches to localize pollutant emission sources with open-path Fourier transform infrared (OP-FTIR) spectroscopy. The first approach combined the plume's peak location information reconstructed from the Smooth Basis Function Minimization (SBFM) algorithm and the wind direction data to calculate source projection lines. In the second approach, the plume's peak location was determined with the Monte Carlo methodology by randomly sampling within the beam segment having the largest path-integrated concentration. We first conducted a series of simulation studies to investigate the sensitivity of using different basis functions in the SBFM algorithm. It was found that fitting with the beta and Weibull basis functions generally gave better estimates of the peak locations than with the normal basis function when the plumes were mainly within the OP-FTIR's monitoring line. However, for plumes that were symmetric to the peak position or spread over the OP-FTIR, fitting with the normal basis function gave better performance. In the field experiment, two tracer gases were released simultaneously from two locations and the OP-FTIR collected data downwind from the sources with a maximum beam path length of 97 m. For the first approach, the release locations were within the 0.25- to 0.5-probability area only after the uncertainty of the peak locations was included in the calculation process. The second approach was easy to implement and still performed as satisfactorily as the first approach. The distances from the sources to the best-fit lines (i.e., the regression lines) of the estimated locations were smaller than 10 m.     

Loading estimates of lead, copper, cadmium, and zinc in urban runoff from specific sources

Urban stormwater runoff is being recognized as a substantial source of pollutants to receiving waters. A number of investigators have found significant levels of metals in runoff from urban areas, especially in highway runoff. As an initiatory study, this work estimates lead, copper, cadmium, and zinc loadings from various sources in a developed area utilizing information available in the literature, in conjunction with controlled experimental and sampling investigations. Specific sources examined include building siding and roofs; automobile brakes, tires, and oil leakage; and wet and dry atmospheric deposition. Important sources identified are building siding for all four metals, vehicle brake emissions for copper and tire wear for zinc. Atmospheric deposition is an important source for cadmium, copper, and lead. Loadings and source distributions depend on building and automobile density assumptions and the type of materials present in the area examined. Identified important sources are targeted for future comprehensive mechanistic studies. Improved information on the metal release and distributions from the specific sources, along with detailed characterization of watershed areas will allow refinements in the predictions.     

Indoor pollutant mixing time in an isothermal closed room: an investigation using CFD

We report on computational fluid dynamics (CFD) predictions of mixing time of a pollutant in an unventilated, mechanically mixed, isothermal room. The study aims to determine: (1) the adequacy of the standard Reynolds Averaged Navier Stokes two-equation (k−) turbulence model for predicting the mixing time under these conditions and (2) the extent to which the mixing time depends on the room airflow, rather than the source location within the room. The CFD simulations modeled the 12 mixing time experiments performed by Drescher et al. (Indoor Air 5 (1995) 204) using a point pulse release in an isothermal, sealed room mechanically mixed with variable power blowers. Predictions of mixing time were found in good agreement with experimental measurements, over an order of magnitude variation in blower power. Additional CFD simulations were performed to investigate the relation between pollutant mixing time and source location. Seventeen source locations and five blower configurations were investigated. Results clearly show large dependence of the mixing time on the room airflow, with some dependence on source location. We further explore dependence of mixing time on the velocity and turbulence intensity at the source location. Implications for positioning air-toxic sensors in rooms are briefly discussed.     

Influence of indoor transport and mixing time scales on the performance of sensor systems for characterizing contaminant releases

Optimizing real-time sensor systems to detect and identify relevant characteristics of an indoor contaminant event is a challenging task. The interpretation of incoming sensor data is confounded by uncertainties in building operation, in the forces driving contaminant transport, and in the physical parameters governing transport. In addition, simulation tools used by the sensor interpretation algorithm introduce modeling uncertainties. This paper explores how the time scales inherent in contaminant transport influence the information that can be extracted from real-time sensor data. In particular, we identify three time scales (within room mixing, room-to-room transport, and removal from the building) and study how they affect the ability of a Bayesian Monte Carlo (BMC) sensor interpretation algorithm to identify the release location and release mass from a set of experimental data, recorded in a multi-floor building. The research shows that some limitations in the BMC approach do not depend on details of the models or the algorithmic implementation, but rather on the physics of contaminant transport. These inherent constraints have implications for the design of sensor systems.     

A statistical assessment of saturation and mobile sampling strategies to estimate long-term average concentrations across urban areas

The objectives of this study were: (1) to quantify the errors associated with saturation air quality monitoring in estimating the long-term (i.e., annual and 5 yr) mean at a given site from four 2-week measurements, once per season; and (2) to develop a sampling strategy to guide the deployment of mobile air quality facilities for characterizing intraurban gradients of air pollutants, that is, to determine how often a given location should be visited to obtain relatively accurate estimates of the mean air pollutant concentrations. Computer simulations were conducted by randomly sampling ambient monitoring data collected in six Canadian cities at a variety of settings (e.g., population-based sites, near-roadway sites). The 5-yr (1998-2002) dataset consisted of hourly measurements of nitric oxide (NO), nitrogen dioxide (NO2), oxides of nitrogen (NOx), sulfur dioxide (SO2), coarse particulate matter (PM10), fine particulate matter (PM2.5), and CO. The strategy of randomly selecting one 2-week measurement per season to determine the annual or long-term average concentration yields estimates within 30% of the true value 95% of the time for NO2, PM10 and NOx. Larger errors, up to 50%, are expected for NO, SO2, PM2.5, and CO. Combining concentrations from 85 random 1-hr visits per season provides annual and 5-yr average estimates within 30% of the true value with good confidence. Overall, the magnitude of error in the estimates was strongly correlated with the variability of the pollutant. A better estimation can be expected for pollutants known to be less temporally variable and/or over geographic areas where concentrations are less variable. By using multiple sites located in different settings, the relationships determined for estimation error versus number of measurement periods used to determine long-term average are expected to realistically portray the true distribution. Thus, the results should be a good indication of the potential errors one could expect in a variety of different cities, particularly in more northern latitudes.     

Application of Airborne Lidar in Particulate Air Quality Problem Delineation,Monitoring Network Design and Control Strategy Development

Airborne lidar (light detection and ranging) data were collected during two field programs in 1989 in the border metropolitan area comprising El Paso (U.S.) and Ciudad Juarez (Mexico). These data were utilized to provide estimates of suspended particulate distributions in the presence of historical data with limited spatial and temporal density in the U.S. and almost no historical data in Mexico. The resulting information was used to help ascertain the magnitude and spatial extent of the particulate air quality problem and to help determine the number and disposition of recommended permanent particulate monitors in the area. The results support the hypothesis that, given a few representative measurements for calibration, airborne lidar might be used in specific situations to develop detailed concentration patterns and to provide useful estimates of pollutant flux, when combined with appropriate measurements of wind velocity profiles. Such information, in turn, should be useful for the development of control strategies for potential alleviation of particulate air quality problems.     

Traffic and meteorological impacts on near-road air quality: summary of methods and trends from the Raleigh Near-Road Study

A growing number of epidemiological studies conducted worldwide suggest an increase in the occurrence of adverse health effects in populations living, working, or going to school near major roadways. A study was designed to assess traffic emissions impacts on air quality and particle toxicity near a heavily traveled highway. In an attempt to describe the complex mixture of pollutants and atmospheric transport mechanisms affecting pollutant dispersion in this near-highway environment, several real-time and time-integrated sampling devices measured air quality concentrations at multiple distances and heights from the road. Pollutants analyzed included U.S. Environmental Protection Agency (EPA)-regulated gases, particulate matter (coarse, fine, and ultrafine), and air toxics. Pollutant measurements were synchronized with real-time traffic and meteorological monitoring devices to provide continuous and integrated assessments of the variation of near-road air pollutant concentrations and particle toxicity with changing traffic and environmental conditions, as well as distance from the road. Measurement results demonstrated the temporal and spatial impact of traffic emissions on near-road air quality. The distribution of mobile source emitted gas and particulate pollutants under all wind and traffic conditions indicated a higher proportion of elevated concentrations near the road, suggesting elevated exposures for populations spending significant amounts of time in this microenvironment. Diurnal variations in pollutant concentrations also demonstrated the impact of traffic activity and meteorology on near-road air quality. Time-resolved measurements of multiple pollutants demonstrated that traffic emissions produced a complex mixture of criteria and air toxic pollutants in this microenvironment. These results provide a foundation for future assessments of these data to identify the relationship of traffic activity and meteorology on air quality concentrations and population exposures.     

Measurement of the dispersion of a smoke plume at large distances from the source

The width of a smoke plume from an isolated ore smelter in the tropics has been found from airborne measurements of total particle concentrations at distances up to 560 km from the source under cloudless, inversion-limited, convective conditions over flat terrain. Results are surprisingly similar to estimates derived from the few available relevant experiments, most of which were conducted in different meteorological circumstances and using quite different techniques.     

Traffic and Meteorological Impacts on Near-Road Air Quality: Summary of Methods and Trends from the Raleigh Near-Road Study

Abstract

A growing number of epidemiological studies conducted worldwide suggest an increase in the occurrence of adverse health effects in populations living, working, or going to school near major roadways. A study was designed to assess traffic emissions impacts on air quality and particle toxicity near a heavily traveled highway. In an attempt to describe the complex mixture of pollutants and atmospheric transport mechanisms affecting pollutant dispersion in this near-highway environment, several real-time and time-integrated sampling devices measured air quality concentrations at multiple distances and heights from the road. Pollutants analyzed included U.S. Environmental Protection Agency (EPA)-regulated gases, particulate matter (coarse, fine, and ultrafine), and air toxics. Pollutant measurements were synchronized with real-time traffic and meteorological monitoring devices to provide continuous and integrated assessments of the variation of near-road air pollutant concentrations and particle toxicity with changing traffic and environmental conditions, as well as distance from the road. Measurement results demonstrated the temporal and spatial impact of traffic emissions on near-road air quality. The distribution of mobile source emitted gas and particulate pollutants under all wind and traffic conditions indicated a higher proportion of elevated concentrations near the road, suggesting elevated exposures for populations spending significant amounts of time in this microenvironment. Diurnal variations in pollutant concentrations also demonstrated the impact of traffic activity and meteorology on near-road air quality. Time-resolved measurements of multiple pollutants demonstrated that traffic emissions produced a complex mixture of criteria and air toxic pollutants in this microenvironment. These results provide a foundation for future assessments of these data to identify the relationship of traffic activity and meteorology on air quality concentrations and population exposures.     

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.     

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.     

The Limitations of Air Quality Standards

This paper describes a diffusion model designed to permit calculation of seasonal average concentrations of an air pollutant, in particular, sulfur dioxide. The calculations can encompass multiple sources and multiple receptors. For each receptor location the model sums the effect of all sources over a wide range of meteorological conditions. Input data include source pollutant emissions, source configuration and location, receptor location, and meteorological data expressed as a joint frequency distribution of wind direction, wind speed, stability. To determine the model’s accuracy, concentration estimates for St. Louis, Mo., are compared with measured SO₂ concentrations. The overall correlation with observed data is satisfactory. A computer program to handle the numerous calculations was written in Fortran IV language for use on an IBM 1130 computer.     

Evaluating the capabilities of Aerosol-to-Liquid Particle Extraction System (ALPXS)/ICP-MS for monitoring trace metals in indoor air

This study investigates the application of the Aerosol-to-Liquid Particle Extraction System (ALPXS), which uses wet electrostatic precipitation to collect airborne particles, for multi-element indoor stationary monitoring. Optimum conditions are determined for capturing airborne particles for metal determination by inductively coupled plasma–mass spectrometry (ICP-MS), for measuring field blanks, and for calculating limits of detection (LOD) and quantification (LOQ). Due to the relatively high flow rate (300 L min^?1), a sampling duration of 1 hr to 2 hr was adequate to capture airborne particle-bound metals under the investigated experimental conditions. The performance of the ALPXS during a building renovation demonstrated signal-to-noise ratios appropriate for sampling airborne particles in environments with elevated metal concentrations, such as workplace settings. The ALPXS shows promise as a research tool for providing useful information on short-term variations (transient signals) and for trapping particles into aqueous solutions where needed for subsequent characterization. As the ALPXS does not provide size-specific samples, and its efficiency at different flow rates has yet to be quantified, the ALPXS would not replace standard filter-based protocols accepted for regulatory applications (e.g., exposure measurements), but rather would provide additional information if used in conjunction with filter based methods.

ImplicationsThis study investigates the capability of the Aerosol-to-Liquid Particle Extraction System (ALPXS) for stationary sampling of airborne metals in indoor workplace environments, with subsequent analysis by ICP-MS. The high flow rate (300 L/min) permits a short sampling duration (< 2 hr). Results indicated that the ALPXS was capable of monitoring short-term changes in metal emissions during a renovation activity. This portable instrument may prove to be advantageous in occupational settings as a qualitative indicator of elevated concentrations of airborne metals at short time scales.     

A Statistical Assessment of Saturation and Mobile Sampling Strategies to Estimate Long-Term Average Concentrations across Urban Areast

Abstract

The objectives of this study were: (1) to quantify the errors associated with saturation air quality monitoring in estimating the long-term (i.e., annual and 5 yr) mean at a given site from four 2-week measurements, once per season; and (2) to develop a sampling strategy to guide the deployment of mobile air quality facilities for characterizing intraurban gradients of air pollutants, that is, to determine how often a given location should be visited to obtain relatively accurate estimates of the mean air pollutant concentrations. Computer simulations were conducted by randomly sampling ambient monitoring data collected in six Canadian cities at a variety of settings (e.g., population-based sites, near-roadway sites). The 5-yr (1998–2002) dataset consisted of hourly measurements of nitric oxide (NO), nitrogen dioxide (NO₂), oxides of nitrogen (NO_x), sulfur dioxide (SO₂), coarse particulate matter (PM₁₀), fine particulate matter (PM_2.5)_, and CO. The strategy of randomly selecting one 2-week measurement per season to determine the annual or long-term average concentration yields estimates within 30% of the true value 95% of the time for NO₂, PM₁₀ and NO_x. Larger errors, up to 50%, are expected for NO, SO₂, PM_2.5, and CO. Combining concentrations from 85 random 1-hr visits per season provides annual and 5-yr average estimates within 30% of the true value with good confidence. Overall, the magnitude of error in the estimates was strongly correlated with the variability of the pollutant. A better estimation can be expected for pollutants known to be less temporally variable and/or over geographic areas where concentrations are less variable. By using multiple sites located in different settings, the relationships determined for estimation error versus number of measurement periods used to determine long-term average are expected to realistically portray the true distribution. Thus, the results should be a good indication of the potential errors one could expect in a variety of different cities, particularly in more northern latitudes.     

Assessing the representativeness of monitoring data from an urban intersection site in central London,UK

The wind flow field around urban street-building configurations has an important influence on the microscale pollutant dispersion from road traffic, affecting overall dilution and creating localised spatial variations of pollutant concentration. As a result, the “representativeness” of air quality measurements made at different urban monitoring sites can be strongly dependent on the interaction of the local wind flow field with the street-building geometry surrounding the monitor. The present study is an initial attempt to develop a method for appraising the significance of air quality measurements from urban monitoring sites, using a general application computational fluid dynamics (CFD) code to simulate small-scale flow and dispersion patterns around real urban building configurations. The main focus of the work was to evaluate routine CO monitoring data collected by Westminster City Council at an intersection of street canyons at Marylebone Road, Central London. Many monitors in the UK are purposely situated at urban canyon intersections, which are thought to be local “hot spots” of pollutant emissions, however very limited information exists in the literature on the flow and dispersion patterns associated with them. With the use of simple CFD simulations and the analysis of available monitoring data, it was possible to gain insights into the effect of wind direction on the small-scale dispersion patterns at the chosen intersection, and how that can influence the data captured by a monitor. It was found that a change in wind direction could result in an increase or decrease of monitored CO concentration of up to 80%, for a given level of traffic emissions and meteorological conditions. Understanding and de-coupling the local effect of wind direction from monitoring data using the methods presented in this work could prove a useful new tool for urban monitoring data interpretation.     

Precision of atmospheric dry deposition data from the Clean Air Status and Trends Network

A collocated, dry deposition sampling program was begun in January 1987 by the US Environmental Protection Agency to provide ongoing estimates of the overall precision of dry deposition and supporting data entering the Clean Air Status and Trends Network (CASTNet) archive. Duplicate sets of dry deposition sampling instruments were installed adjacent to existing instruments and have been operated for various periods at 11 collocated field sites. All sampling and operations were performed using standard CASTNet procedures. The current study documents the bias-corrected precision of CASTNet data based on collocated measurements made at paired sampling sites representative of sites across the network. These precision estimates include the variability for all operations from sampling to data storage in the archive. Precision estimates are provided for hourly, instrumental ozone (O₃) concentration and meteorological measurements, hourly model estimates of deposition velocity (V_d) from collocated measurements of model inputs, hourly O₃ deposition estimates, weekly filter pack determinations of selected atmospheric chemical species, and weekly estimates of V_d and deposition for each monitored filter pack chemical species and O₃.Estimates of variability of weekly pollutant concentrations, expressed as coefficients of variation, depend on chemical species: NO₃⁻∼8.1%; HNO₃∼6.4%; SO₂∼4.3%; NH₄⁺∼3.7%; SO₄²⁻∼2.3%; and O₃∼1.3%. Precision of estimates of weekly V_d from collocated measurements of model inputs also depends on the chemical species: aerosols ∼2.8%; HNO₃∼2.6%; SO₂∼3.0%; and O₃∼2.0%. Corresponding precision of weekly deposition estimates are: NO₃⁻∼8.6%; HNO₃⁻∼5.2%; SO₂∼5.6%; NH₄⁺∼3.9%; SO₄²⁻∼3.5%; and O₃∼3.3%. Precision of weekly concentration, V_d estimates, and deposition estimates are comparable in magnitude and slightly smaller than the corresponding hourly values. Annual precision estimates, although uncertain due to their small sample size in the current study, are consistent with the corresponding weekly values.     

Predicting size-resolved particle behavior in multizone buildings

We compare model predictions to measurements of SF₆ and environmental tobacco smoke particle concentrations in a three-room chamber experiment. To make predictions of multi-room aerosol transport and fate, we linked a multizone airflow model (COMIS) with an indoor aerosol dynamics model (MIAQ4). The linked models provide improved simulation capabilities for predicting aerosol concentrations and exposures in buildings. In this application, we found that the multizone air flow model was vital for predicting the inter-room airflows due to temperature differences between the rooms and when air-sampling pumps were operating during the experiment. Model predictions agree well with measurements, as shown by several comparison metrics. However, predictions of airborne ETS concentrations are slightly lower than measurements. This is mostly attributable to under-stating the source release amount, which we specified independently from literature estimates. Model predictions of ETS particle-size distributions agree with measurements; size bins with the peak concentrations are slightly over-predicted initially, but agree thereafter.