Modeling exposure to particulate matter

Exposure assessment, a component of risk assessment, links sources of pollution with health effects. Exposure models are scientific tools used to gain insights into the processes affecting exposure assessment. The purpose of this paper is to review the process and methodology of estimating inhalation exposure to particulate matter (PM) using various types of models. Three types of models are discussed in the paper. Indirect type of models are physical models that employ inventories of outdoor and indoor sources and their emission rates to identify major sources contributing to exposure to PM, and use fate and transport and indoor air quality models to estimate PM concentrations at receptor sites. PM concentrations and time spent by a subject at each receptor site are input variables to the conventional exposure model that estimates the desired exposure levels. Direct type models use measured exposure or exposure concentrations in conjunction with information obtained from questionnaires to formulate exposure regression models. Stochastic models use exposure measurements, estimates can also be used, to formulate exposure population distributions and investigate associated uncertainty and variability. Since models developed using databases from western countries are not necessarily applicable in developing countries, the difference in requirements among western and developing countries is highlighted in the paper. Employment of exposure modeling methods in developing countries requires development of local information. Such information includes local outdoor and indoor source inventories, local or regional meteorological conditions, adjustment of indoor models to reflect local building construction conditions, and use of questionnaires to obtain local time budget and activity patterns of the subject population.     

Estimating Emissions from Air Concentration Measurements

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

Refining emission rate estimates using a coupled receptor–dispersion modeling approach

Receptor modeling techniques like chemical mass balance are used to attribute pollution levels at a point to different sources. Here we analyze the composition of particulate matter and use the source profiles of sources prevalent in a region to estimate quantitative source contributions. In dispersion modeling on the other hand the emission rates of various sources together with meteorological conditions are used to determine the concentrations levels at a point or in a region. The predictions using these two approaches are often inconsistent. In this work these differences are attributed to errors in emission inventory. Here an algorithm for coupling receptor and dispersion models is proposed to reduce the differences of the two predictions and determine the emission rates accurately. The proposed combined approach helps reconcile the differences arising when the two approaches are used in a stand-alone mode. This work is based on assuming that the models are perfect and uses a model-to-model comparison to illustrate the concept.     

Predicting residential indoor concentrations of nitrogen dioxide,fine particulate matter,and elemental carbon using questionnaire and geographic information system based data

Previous studies have identified associations between traffic-related air pollution and adverse health effects. Most have used measurements from a few central ambient monitors and/or some measure of traffic as indicators of exposure, disregarding spatial variability and factors influencing personal exposure-ambient concentration relationships. This study seeks to utilize publicly available data (i.e., central site monitors, geographic information system, and property assessment data) and questionnaire responses to predict residential indoor concentrations of traffic-related air pollutants for lower socioeconomic status (SES) urban households.As part of a prospective birth cohort study in urban Boston, we collected indoor and outdoor 3–4 day samples of nitrogen dioxide (NO₂) and fine particulate matter (PM_2.5) in 43 low SES residences across multiple seasons from 2003 to 2005. Elemental carbon (EC) concentrations were determined via reflectance analysis. Multiple traffic indicators were derived using Massachusetts Highway Department data and traffic counts collected outside sampling homes. Home characteristics and occupant behaviors were collected via a standardized questionnaire. Additional housing information was collected through property tax records, and ambient concentrations were collected from a centrally located ambient monitor.The contributions of ambient concentrations, local traffic and indoor sources to indoor concentrations were quantified with regression analyses. PM_2.5 was influenced less by local traffic but had significant indoor sources, while EC was associated with traffic and NO₂ with both traffic and indoor sources. Comparing models based on covariate selection using p-values or a Bayesian approach yielded similar results, with traffic density within a 50 m buffer of a home and distance from a truck route as important contributors to indoor levels of NO₂ and EC, respectively. The Bayesian approach also highlighted the uncertanity in the models. We conclude that by utilizing public databases and focused questionnaire data we can identify important predictors of indoor concentrations for multiple air pollutants in a high-risk population.     

Emission rates of chlorinated volatile organics from new and used consumer products found during vapor intrusion investigations: Impact on indoor air concentrations

Consumer products can emit chlorinated volatile organic compounds (CVOCs) that complicate vapor intrusion (VI) assessments. Assessment protocols acknowledge the need to remove these products during VI investigations, but they can be problematic to identify and locate. Predicting if the products cause detectable air concentrations is also difficult since emission rate information is limited and can vary with product use and age. In this study, the emission rates of 1,2-dichloroethane, trichloroethene, tetrachloroethene, and carbon tetrachloride from four consumer products identified as indoor sources during VI field investigations were measured under laboratory conditions using a flow through system. Emissions of PCE from an adhesive container tube ranged from 1.33 ± 1.13 μg/min (unopened) to 23.9 ± 2.93 μg/min (previously opened). The laboratory-measured emission rates were used to estimate indoor air concentrations, which were then compared to concentrations measured after the products placed were into an actual residence. The estimated and measured indoor air concentrations were generally comparable, showing that emission rate information can be used to determine the relative impact of internal CVOC sources.     

Characterization of particulate matter concentrations during controlled indoor activities

Indoor sources have been identified as a major contributor to the increase of particle concentration in indoor environments. The work presented here is a study of the characteristics of particulate matter number size distribution and mass concentration under controlled indoor activities in a laboratory room. The objective is to characterize particulate matter concentrations indoors resulted under the influence of specific sources. Measurements were performed in an empty laboratory (period September–October 2006) using a GRIMM SMPS+C system (particle size range between 11.1 and 1083.3 nm), a DustTrak Aerosol Monitor (TSI) and a P-Trak Ultrafine Particle Counter (TSI). The studied indoor activities included candle burning, hot plate heating, water boiling, onion frying, vacuuming, hair drying, hair spraying, smoking and burning of incense stick. The AMANpsd computer algorithm was used to evaluate the modal structure of measured particle number size distribution data. Furthermore, the change of the particle number size distribution shape under the influence of different emission sources was studied versus time. Finally the particle emission rates were computed. High particle number concentrations were observed during smoking, onion frying, candle burning and incense stick burning. The highest particle mass concentrations were measured during smoking and hair spraying. The shift of the particle size distribution to larger diameters suggests the presence of strong coagulation effect during candle burning, incense stick burning, smoking and onion frying. The size distribution was mainly bimodal during onion frying and candle burning, whereas the size distribution remained unimodal during incense stick burning and smoking experiments.     

Application of dispersion modeling to indoor gas release scenarios

Many complex models are available to study the dispersion of contaminants or ventilation effectiveness in indoor spaces. Because of the computationally complex numerical schemes employed, most of these models require mainframe computers or workstations. However, simple design tools or guidelines are needed, in addition to complicated models. A dispersion model based on the basic governing equations was developed and uses an analytical solution. Because the concentration is expressed by an analytical solution, the grid size and time steps are user definable. A computer program was used to obtain numerical results and to obtain release history from a thermodynamic source model. The model can be used to estimate three-dimensional spatial and temporal variations in concentrations resulting from transient gas releases in an enclosure. The model was used to study a gas release scenario from a pressurized cylinder into a large ventilated building, in this case, a transit parking and fueling facility.     

Potential for reducing indoor styrene exposure from copied paper through use of low-emitting toners

Tests were conducted using 53-L dynamic chambers to determine airborne styrene emission rates over time from freshly copied paper. Copies were produced on a single photocopier using two toners manufactured for this copier but having different styrene contents. The resulting emission models were used to predict whether indoor styrene concentrations resulting from copied paper in a typical office might be significantly reduced by use of a low-emitting toner for a given copier. The styrene emissions were best represented by either a 3rd-order decay model or by a power law model having an exponent between 0.3 and 0.5 (R2 = 0.94-0.99). The two toners resulted in copied paper having significantly different styrene emissions (p < 0.01), with unit mass emissions over 1000 hr being nine times greater with the higher-emitting toner. But copied paper is predicted to produce peak indoor styrene concentrations in a typical office no more than 1% of the World Health Organization health-based guideline. Thus, for the toners considered here, indoor styrene exposures from copied paper appear to be too limited to provide incentive for switching to the lower-emitting toner. The ability to generalize these conclusions is limited by the fact that only one copier and two toners could be tested.     

Analytical solutions to compartmental indoor air quality models with application to environmental tobacco smoke concentrations measured in a house

This paper derives the analytical solutions to multi-compartment indoor air quality models for predicting indoor air pollutant concentrations in the home and evaluates the solutions using experimental measurements in the rooms of a single-story residence. The model uses Laplace transform methods to solve the mass balance equations for two interconnected compartments, obtaining analytical solutions that can be applied without a computer. Environmental tobacco smoke (ETS) sources such as the cigarette typically emit pollutants for relatively short times (7-11 min) and are represented mathematically by a "rectangular" source emission time function, or approximated by a short-duration source called an "impulse" time function. Other time-varying indoor sources also can be represented by Laplace transforms. The two-compartment model is more complicated than the single-compartment model and has more parameters, including the cigarette or combustion source emission rate as a function of time, room volumes, compartmental air change rates, and interzonal air flow factors expressed as dimensionless ratios. This paper provides analytical solutions for the impulse, step (Heaviside), and rectangular source emission time functions. It evaluates the indoor model in an unoccupied two-bedroom home using cigars and cigarettes as sources with continuous measurements of carbon monoxide (CO), respirable suspended particles (RSP), and particulate polycyclic aromatic hydrocarbons (PPAH). Fine particle mass concentrations (RSP or PM3.5) are measured using real-time monitors. In our experiments, simultaneous measurements of concentrations at three heights in a bedroom confirm an important assumption of the model-spatial uniformity of mixing. The parameter values of the two-compartment model were obtained using a "grid search" optimization method, and the predicted solutions agreed well with the measured concentration time series in the rooms of the home. The door and window positions in each room had considerable effect on the pollutant concentrations observed in the home. Because of the small volumes and low air change rates of most homes, indoor pollutant concentrations from smoking activity in a home can be very high and can persist at measurable levels indoors for many hours.     

Indirect Source Impact Analysis-Carbon Monoxide Modeling

This paper reviews current methods and models used in estimating the impacts of indirect sources on CO air quality, an important process in rapidly growing areas. The paper gives an overview of the modeling process, reviews how to obtain fleet average emission factors, presents a commonly used set of worst-case meteorology, identifies dispersion models available for predicting local CO concentrations and tells how to predict an 8-hour average CO concentration given a 1-hour prediction. The paper also discusses background CO concentrations and some of the issues involved in choosing reasonable receptor locations. Several problems exist with indirect source impact analysis—in both the technical area and the policy area. Increased effort is needed to correct these problems, especially to quantify the probability of the worst-case meteorology and to define the locations of reasonable receptors.     

Characterization of model error in a simulation of fine particulate matter exposure distributions of the working age population in Helsinki, Finland

Exposure models are needed for comparison of scenarios resulting from alternative policy options. The reliability of models used for such purposes should be quantified by comparing model outputs in a real situation with the corresponding observed exposures. Measurement errors affect the observations, but if the distribution of these errors for single observations is known, the bias caused for the population statistics can be corrected. The current paper does this and calculates model errors for a probabilistic simulation of 48-hr fine particulate matter (PM2.5) exposures. Direct and nested microenvironment-based models are compared. The direct model requires knowledge on the distribution of the indoor concentrations, whereas the nested model calculates indoor concentrations from ambient levels, using infiltration factors and indoor sources. The model error in the mean exposure level was <0.5 microg m(-3) for both models. Relative errors in the estimated population mean were +1% and -5% for the direct and nested models, respectively. Relative errors in the estimated SD were -9% and -23%, respectively. The magnitude of these errors and the errors calculated for population percentiles indicate that the model errors would not drive general conclusions derived from these models, supporting the use of the models as a tool for evaluation of potential exposure reductions in alternative policy scenarios.     

Outdoor versus indoor contributions to indoor particulate matter (PM) determined by mass balance methods

This study compares an indoor-outdoor air-exchange mass balance model (IO model) with a chemical mass balance (CMB) model. The models were used to determine the contribution of outdoor sources and indoor resuspension activities to indoor particulate matter (PM) concentrations. Simultaneous indoor and outdoor measurements of PM concentration, chemical composition, and air-exchange rate were made for five consecutive days at a single-family residence using particle counters, nephelometers, and filter samples of integrated PM with an aerodynamic diameter of less than or equal to 2.5 microm (PM2.5) and PM with an aerodynamic diameter of less than or equal to 5 microm (PM5). Chemical compositions were determined by inductively coupled plasma mass-spectrometry. During three high-activity days, prescribed activities, such as cleaning and walking, were conducted over a period of 4-6 hr. For the remaining two days, indoor activities were minimal. Indoor sources accounted for 60-89% of the PM2.5 and more than 90% of the PM5 for the high-activity days. For the minimal-activity days, indoor sources accounted for 27-47% of PM2.5 and 44-60% of the PM5. Good agreement was found between the two mass balance methods. Indoor PM2.5 originating outdoors averaged 53% of outdoor concentrations.     

Indoor/Outdoor relationships, trends, and carbonaceous content of fine particulate matter in retirement homes of the Los Angeles Basin

Hourly indoor and outdoor fine particulate matter (PM2.5), organic and elemental carbon (OC and EC, respectively), particle number (PN), ozone (O3), carbon monoxide (CO), and nitrogen oxide (NOx) concentrations were measured at two different retirement communities in the Los Angeles, CA, area as part of the Cardiovascular Health and Air Pollution Study. Site A (group 1 [G1]) was operated from July 6 to August 20, 2005 (phase 1 [P1]) and from October 19 to December 10, 2005 (P2), whereas site B (group 2 [G2]) was operated from August 24 to October 15, 2005 (P1), and from January 4 to February 18, 2006 (P2). Overall, the magnitude of indoor and outdoor measurements was similar, probably because of the major influence of outdoor sources on indoor particle and gas levels. However, G2 showed a substantial increase in indoor OC, PN, and PM2.5 between 6:00 and 9:00 a.m., probably from cooking. The contributions of primary and secondary OC (SOA) to measured outdoor OC were estimated from collected OC and EC concentrations using EC as a tracer of primary combustion-generated OC (i.e., "EC tracer method"). The study average outdoor SOA accounted for 40% of outdoor particulate OC (40-45% in the summer and 32-40% in the winter). Air exchange rates (hr(-1)) and infiltration factors (Finf; dimensionless) at each site were also determined. Estimated Finf and measured particle concentrations were then used in a single compartment mass balance model to assess the contributions of indoor and/or outdoor sources to measured indoor OC, EC, PM2.5, and PN. The average percentage contributions of indoor SOA of outdoor origin to measured indoor OC were approximately 35% (during G1P1 and G1P2) and approximately 45% (for G2P1 and G2P2). On average, 36% (G2P1) to 44% (G1P1) of measured indoor OC was composed of outdoor-generated primary OC.     

Analysis of air quality observations with the aid of the source-receptor relationship approach

In this study, an attempt was made to analyze time series of air quality measurements (O3, SO2, SO4(2-), NOx) conducted at a remote place in the eastern Mediterranean (Finokalia at Crete Island in 1999) to obtain concrete information on potential contributions from emission sources. For the definition of a source-receptor relationship, advanced meteorological and dispersion models appropriate to identify "areas of influence" have been used. The model tools used are the Regional Atmospheric Modeling System and the Lagrangian-type particle dispersion model (forward and backward in time), with capabilities to derive influence functions and definition of "areas of influence." When high levels of pollutants have been measured at the remote location of Finokalia, particles are released from this location (receptor) and traced backward in time. The influence function derived from particle distributions characterizes dispersion conditions in the atmosphere and also provides information on potential contributions from emission sources within the modeling domain to this high concentration. As was shown in the simulation results, the experimental site of Finokalia in Crete is influenced during the selected case studies, primarily by pollutants emitted from the urban conglomerate of Athens. Secondarily, it is influenced by polluted air masses arriving from Italy and/or the Black Sea Region. For some specific cases, air pollutants monitored at Finokalia were possibly related to war activities in the West Balkan Region (Kosovo).     

The use of indoor air measurements to evaluate intrusion of subsurface VOC vapors into buildings

The implementation of a risk-based corrective action approach often requires consideration of soil vapor migration into buildings and potential inhalation exposure and risk to human health. Due to the uncertainty associated with models for this pathway, there may be a desire to analyze indoor air samples to validate model predictions, and this approach is followed on a somewhat frequent basis at sites where risks are considered potentially significant. Indoor air testing can be problematic for a number of reasons. Soil vapor intrusion into buildings is complex, highly dependent on site-specific conditions, and may vary over time, complicating the interpretation of indoor air measurements when the goal is to deduce the subsurface-derived component. An extensive survey of indoor air quality data sets highlights the variability in indoor volatile organic compound (VOC) concentrations and numerous sources that can lead to elevated VOC levels. The contribution from soil vapor is likely to be small relative to VOCs from other sources for most sites. In light of these challenges, we discuss how studies that use indoor air testing to assess subsurface risks could be improved. To provide added perspective, we conclude by comparing indoor air concentrations and risks arising from subsurface VOCs, predicted using standard model equations for soil vapor fate and intrusion into buildings, to those associated with indoor sources.     

Chemical Mass Balance Model with Fractionation for Apportioning PM2.5: A Test Case for Los Angeles Traffic Sources

Abstract

Chemical mass balance receptor models (CMBs) use measured pollutant concentrations, along with source information, to apportion the contributions of primary sources to the measured concentrations. CMBs can be used to evaluate the accuracy of the emission inventories that underlie State Implementation Plan (SIP) modeling, by providing an allocation of emissions to individual source categories. CMBs, however, traditionally have not accounted for the chemical reaction and differential deposition or fractionation that occur between the source and receptor. This means that they have historically had severe limitations in apportioning secondary particulate matter (PM), which is an especially important component of fine PM (PM_2.5). Stafford and Liljestrand developed a method to account for fractionation in CMBs using depletion factors based on a solution of the steady-state advection-dispersion equation, including gravitational settling, dry deposition, and first-order chemical reaction. In the research presented here, the method of Stafford and Liljestrand was tested using gaseous and PM ambient concentration data from the Los Angeles, CA, air shed, along with traffic source profiles specific to Los Angeles and the CMB7 receptor model of the U.S. Environment Protection Agency. Including fractionation increased nitrate apportioned from 5% and 6% to 83% and 86% for Claremont, CA, and Long Beach, CA, respectively. This is significant, because CMBs have historically had difficulty apportioning nitrate. Including fractionation increased the ammonium apportioned by a factor of 7. The method could be used in future case studies to apportion secondary organic carbon as well.     

Evaluating the use of outputs from comprehensive meteorological models in air quality modeling applications

Currently used dispersion models, such as the AMS/EPA Regulatory Model (AERMOD), process routinely available meteorological observations to construct model inputs. Thus, model estimates of concentrations depend on the availability and quality of meteorological observations, as well as the specification of surface characteristics at the observing site. We can be less reliant on these meteorological observations by using outputs from prognostic models, which are routinely run by the National Oceanic and Atmospheric Administration (NOAA). The forecast fields are available daily over a grid system that covers all of the United States. These model outputs can be readily accessed and used for dispersion applications to construct model inputs with little processing. This study examines the usefulness of these outputs through the relative performance of a dispersion model that has input requirements similar to those of AERMOD. The dispersion model was used to simulate observed tracer concentrations from a Tracer Field Study conducted in Wilmington, California in 2004 using four different sources of inputs: (1) onsite measurements; (2) National Weather Service measurements from a nearby airport; (3) readily available forecast model outputs from the Eta Model; and (4) readily available and more spatially resolved forecast model outputs from the MM5 prognostic model. The comparison of the results from these simulations indicate that comprehensive models, such as MM5 and Eta, have the potential of providing adequate meteorological inputs for currently used short-range dispersion models such as AERMOD.     

An analysis of continuous black carbon concentrations in proximity to an airport and major roadways

Black carbon (BC), a constituent of particulate matter, is emitted from multiple combustion sources, complicating determination of contributions from individual sources or source categories from monitoring data. In close proximity to an airport, this may include aircraft emissions, other emissions on the airport grounds, and nearby major roadways, and it would be valuable to determine the factors most strongly related to measured BC concentrations. In this study, continuous BC concentrations were measured at five monitoring sites in proximity to a small regional airport in Warwick, Rhode Island from July 2005 to August 2006. Regression was used to model the relative contributions of aircraft and related sources, using real-time flight activity (departures and arrivals) and meteorological data, including mixing height, wind speed and direction. The latter two were included as a nonparametric smooth spatial term using thin-plate splines applied to wind velocity vectors and fit in a linear mixed model framework. Standard errors were computed using a moving-block bootstrap to account for temporal autocorrelation. Results suggest significant positive associations between hourly departures and arrivals at the airport and BC concentrations within the community, with departures having a more substantial impact. Generalized Additive Models for wind speed and direction were consistent with significant contributions from the airport, major highway, and multiple local roads. Additionally, inverse mixing height, temperature, precipitation, and at one location relative humidity, were associated with BC concentrations. Median contribution estimates indicate that aircraft departures and arrivals (and other sources coincident in space and time) contribute to approximately 24–28% of the BC concentrations at the monitoring sites in the community. Our analysis demonstrated that a regression-based approach with detailed meteorological and source characterization can provide insights about source contributions, which could be used to devise control strategies or to provide monitor-based comparisons with source-specific atmospheric dispersion models.     

Exposure of population and microenvironmental distributions of volatile organic compound concentrations in the EXPOLIS study

Determination of volatile organic compounds (VOCs) formed one part of the EU-EXPOLIS project in which the exposure of European urban populations to particles and gaseous pollutants was studied. The EXPOLIS study concentrated on 30 target VOCs selected on the basis of environmental and health significance and usability of the compounds as markers of pollution sources. In the project, 201 subjects in Helsinki, 50 in Athens, 50 in Basel, 50 in Milan and, 50 in Oxford and 50 in Prague were selected for the final exposure sample. The microenvironmental and personal exposure concentrations of VOCs were the lowest in Helsinki and Basel, while the highest concentrations were measured in Athens and Milan; Oxford and Prague were in between. In all cities, home indoor air was the most significant exposure agent. Workplace indoor air concentrations measured in this study were generally lower than the home indoor concentrations and home outdoor air played a minor role as an exposure agent. When estimating the measured personal exposure concentrations using the measured concentrations and time fractions spent at home indoors, at home outdoors, and at the workplace, it could be concluded that these three microenvironments do not fully explain the personal exposure. Other important sources for personal exposure must be encountered, the most important being traffic/transportation and other indoor environments not measured in this study.