The New Standard Environmental Inventory Questionnaire for Estimation of Indoor Concentrations

Several investigators have developed indoor air quality questionnaires for use in field studies. The approach used in many of them have numerous features in common, but most of them are unique in their content (wording, format, item selection). It is thought that indoor air quality research could be greatly advanced if the primary or fundamental questions and instruments could be consolidated. The use of a basic set of "standard" questions would permit intercomparison of results from different research studies. It is generally agreed that environmental inventory questionnaires (EIQ) help to classify, at least in screening, relative concentration estimates, which precede exposure estimation. Thus, such instruments are not equivalent to monitoring for exposure assessment. However, data linkage and mega data bases are important for some comparative analyses of exposure assessment and exposure-response relationships. Standard instruments such as the EIQ are useful as a screening device to precede other tests to allow identification of potentially high exposure situations. They can also amplify information from other tests. General usage of standard questionnaires and protocols can lead to cumulative improvements in data collection, specificity and effectiveness. This has been the rationale for the present efforts by investigators to form a standardized environmental inventory questionnaire, under the auspices of the U.S. Environmental Protection Agency (EPA), Gas Research Institute (GRI), and Electric Power Research Institute (EPRI).     

Field Detector Evaluation of Organic Clay Soils Contaminated with Diesel Fuel

Due to numerous types of uncontrolled petroleum releases into the environment such as leaking storage tanks, spills, and improper disposal of petroleum wastes, there is a need for quicker, more efficient methods to determine the levels of soil contamination for site remediation. The portable field detectors used most often in preliminary site evaluations are the flame ionization detector (FID) and the photoionization detector (PID). This research explored the relationship between these two instruments in analysis of two clay soil sites contaminated with diesel fuel. As in previous research, a log-log linear correlation was found between the PID and FID instruments for diesel fuel-contaminated soil at each site (R 2 > 0.91). Also, the correlation factors (0.64 and 0.60) between the field instruments at each site were found to be similar. It was asserted that either field instrument can be used to delineate the diesel fuel contamination at that site based upon a previously calculated correlation between the two instruments, and an overall numerical correlation between the field instruments can be used at various sites of similar soil and contamination characteristics. The implementation of the relationships between these two instruments could facilitate and accelerate site characterization in the future.     

Assessment of human exposure to ambient particulate matter

Recent epidemiological studies have consistently shown that the acute mortality effects of high concentrations of ambient particulate matter (PM), documented in historic air pollution episodes, may also be occurring at the low to moderate concentrations of ambient PM found in modern urban areas. In London in December 1952, the unexpected deaths due to PM exposure could be identified and counted as integers by the coroners. In modern times, the PM-related deaths cannot be as readily identified, and they can only be inferred as fractional average daily increases in mortality rates using sophisticated statistical filtering and analyses of the air quality and mortality data. The causality of the relationship between exposure to ambient PM and acute mortality at these lower modern PM concentrations has been questioned because of a perception that there is little significant correlation in time between the ambient PM concentrations and measured personal exposure to PM from all sources (ambient PM plus indoor-generated PM). This article shows that the critical factor supporting the plausibility of a linear PM mortality relationship is the expected high correlation in time of people's exposure to PM of ambient origin with measured ambient PM concentrations, as used in the epidemiological time series studies. The presence of indoor and personal sources of PM masks this underlying relationship, leading to confusion in the scientific literature about the strong underlying temporal relationship between personal exposure to PM of ambient origin and ambient PM concentration. The authors show that the sources of PM of non-ambient origin operate independently of the ambient PM concentrations, so that the mortality effect of non-ambient PM, if any, must be independent of the effects of the ambient PM exposures.     

Estimating separately personal exposure to ambient and nonambient particulate matter for epidemiology and risk assessment: why and how

This paper discusses the legal and scientific reasons for separating personal exposure to PM into ambient and nonambient components. It then demonstrates by several examples how well-established models and data typically obtained in exposure field studies can be used to estimate both individual and community average exposure to ambient-generated PM (ambient PM outdoors plus ambient PM that has infiltrated indoors), indoor-generated PM, and personal activity PM. Ambient concentrations are not highly correlated with personal exposure to nonambient PM or total PM but are highly correlated with personal exposure to ambient-generated PM. Therefore, ambient concentrations may be used in epidemiology as an appropriate surrogate for personal exposure to ambient-generated PM. Suggestions are offered as to how exposure to ambient-generated PM may be obtained and used in epidemiology and risk assessment.     

Assessment of Human Exposure to Ambient Particulate Matter

ABSTRACT

Recent epidemiological studies have consistently shown that the acute mortality effects of high concentrations of ambient particulate matter (PM), documented in historic air pollution episodes, may also be occurring at the low to moderate concentrations of ambient PM found in modern urban areas. In London in December 1952, the unexpected deaths due to PM exposure could be identified and counted as integers by the coroners. In modern times, the PM-related deaths cannot be as readily identified, and they can only be inferred as fractional average daily increases in mortality rates using sophisticated statistical filtering and analyses of the air quality and mortality data. The causality of the relationship between exposure to ambient PM and acute mortality at these lower modern PM concentrations has been questioned because of a perception that there is little significant correlation in time between the ambient PM concentrations and measured personal exposure to PM from all sources (ambient PM plus indoor-generated PM).

This article shows that the critical factor supporting the plausibility of a linear PM mortality relationship is the expected high correlation in time of people's exposure to PM of ambient origin with measured ambient PM concentrations, as used in the epidemiological time series studies. The presence of indoor and personal sources of PM masks this underlying relationship, leading to confusion in the scientific literature about the strong underlying temporal relationship between personal exposure to PM of ambient origin and ambient PM concentration. The authors show that the sources of PM of non-ambient origin operate independently of the ambient PM concentrations, so that the mortality effect of non-ambient PM, if any, must be independent of the effects of the ambient PM exposures.     

Estimating Separately Personal Exposure to Ambient and Nonambient Particulate Matter for Epidemiology and Risk Assessment: Why and How

ABSTRACT

This paper discusses the legal and scientific reasons for separating personal exposure to PM into ambient and nonambient components. It then demonstrates by several examples how well-established models and data typically obtained in exposure field studies can be used to estimate both individual and community average exposure to ambient-generated PM (ambient PM outdoors plus ambient PM that has infiltrated indoors), indoor-generated PM, and personal activity PM. Ambient concentrations are not highly correlated with personal exposure to nonambient PM or total PM but are highly correlated with personal exposure to ambient-generated PM. Therefore, ambient concentrations may be used in epidemiology as an appropriate surrogate for personal exposure to ambient-generated PM. Suggestions are offered as to how exposure to ambient-generated PM may be obtained and used in epidemiology and risk assessment.     

Characterizing relationships between personal exposures to VOCs and socioeconomic,demographic, behavioral variables

Socioeconomic and demographic factors have been found to significantly affect time-activity patterns in population cohorts that can subsequently influence personal exposures to air pollutants. This study investigates relationships between personal exposures to eight VOCs (benzene, toluene, ethylbenzene, o-xylene, m-,p-xylene, chloroform, 1,4-dichlorobenzene, and tetrachloroethene) and socioeconomic, demographic, time-activity pattern factors using data collected from the 1999–2000 National Health and Nutrition Examination Survey (NHANES) VOC study. Socio-demographic factors (such as race/ethnicity and family income) were generally found to significantly influence personal exposures to the three chlorinated compounds. This was mainly due to the associations paired by race/ethnicity and urban residence, race/ethnicity and use of air freshener in car, family income and use of dry-cleaner, which can in turn affect exposures to chloroform, 1,4-dichlorobenzene, and tetrachloroethene, respectively. For BTEX, the traffic-related compounds, housing characteristics (leaving home windows open and having an attached garage) and personal activities related to the uses of fuels or solvent-related products played more significant roles in influencing exposures. Significant differences in BTEX exposures were also commonly found in relation to gender, due to associated significant differences in time spent at work/school and outdoors. The coupling of Classification and Regression Tree (CART) and Bootstrap Aggregating (Bagging) techniques were used as effective tools for characterizing robust sets of significant VOC exposure factors presented above, which conventional statistical approaches could not accomplish. Identification of these significant VOC exposure factors can be used to generate hypotheses for future investigations about possible significant VOC exposure sources and pathways in the general U.S. population.     

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.     

Worshippers' exposure to particulate matter in two temples in Taiwan

Worshippers in temples may be exposed to high concentrations of pollutants emitted from incense burning. This work assessed the PM2.5 and PM10 exposures of temple worshippers in Taiwan and explored the important exposure determinants such as numbers of passing visitors and joss sticks in censers, worshipping dates, and temple characteristics. Sampling was conducted on the 1st, 2nd, 15th, and 16th of the lunar month in two temples in Taichung, Taiwan. Research staff took samples by wearing one PM2.5 and one PM10 sampler and imitating worshipping activity. Personal environmental monitors connected to personal pumps with 2-L/min flow rates were used for sampling. PM10 samples were also simultaneously taken outside the temples. The results suggested that burning joss sticks in temples is a significant PM exposure source. The geometric mean of personal exposure was 444 microg/m3 PM2.5 [geometric standard deviation (GSD) = 1.8] and 583 microg/m3 PM10 (GSD = 1.4). The latter was approximately 4-6 times that of roadside concentrations. Exposures on the 1st and 15th (with more visitors and more joss sticks) were about 130 microg/m3 PM2.5 and 249 microg/m3 PM10 higher than those on the 2nd and the 16th. Furthermore, each joss stick in the censer contributed about 0.40 microg/m3 of particles to the worshippers' exposure. In the worst case, PM exposure during one temple visit would account for 11% of the personal exposure in one day.     

Using Multimedia Modeling to Expedite Site Characterization

GOAL, SCOPE AND BACKGROUND: This paper uses two case studies of U.S. Department of Energy nuclear weapons complex installations to illustrate the integration of expedited site characterization (ESC) and multimedia modeling in the remedial action decision making process. CONCEPTUAL SITE MODELS, MULTIMEDIA MODELS, AND EXPEDITED SITE CHARACTERIZATION: Conceptual site models outline assumptions about contaminates and the spatial/temporal distribution of potential receptors. Multimedia models simulate contaminant transport and fate through multiple environmental media, estimate potential human exposure via specific exposure pathways, and estimate the risk of cancer and non-cancer health outcomes. ESC relies on using monitoring data to quantify the key components of an initial conceptual site model that is modified iteratively using the multimedia model. CASE STUDIES: Two case studies are presented that used the ESC approach: Los Alamos National Laboratory (LANL) and Pantex. LANL released radionuclides, metals, and organic compounds, into canyons surrounding the facility. The Pantex Plant has past waste management operations which included burning chemical wastes in unlined pits, burying wastes in unlined landfills, and discharging plant wastewaters into on-site surface waters. CONCLUSIONS: The case studies indicate that using multimedia models with the ESC approach can inform assessors about what, where, and how much site characterization data needs to be collected to reduce the uncertainty associated with risk assessment. Lowering the degree of uncertainty reduces the time and cost associated with assessing potential risk and increases the confidence that decision makers have in the assessments performed.     

Alveolar Breath Sampling and Analysis to Assess Exposures to Methyl Tertiary Butyl Ether (MTBE) During Motor Vehicle Refueling

Abstract

Methyl tertiary butyl ether (MTBE) is added to gasoline (15% by volume) in many areas of the U.S. to help control carbon monoxide emissions from motor vehicles. In this study we present a sampling and analytical methodology that can be used to assess consumers' exposures to MTBE that may result from routine vehicle refueling operations. The method is based on the collection of alveolar breath samples using evacuated one-liter stainless steel canisters and analysis using a gas chromatograph-mass spectrometer equipped with a patented "valveless" cryogenic preconcentrator.

To demonstrate the utility of this approach, a series of breath samples was collected from two individuals (the person pumping the fuel and a nearby observer) immediately before and for 64 min after a vehicle was refueled with premium grade gasoline. Results demonstrate low levels of MTBE in both subjects' breaths before refueling, and levels that increased by a factor of 35 to 100 after the exposure. Breath elimination models fitted to the post exposure measurements indicate that the half-life of MTBE in the first physiological compartment was between 1.3 and 2.9 min. Analysis of the resulting models suggests that breath elimination of MTBE during the 64 min monitoring period was approximately 115 jug for the refueling subject while it was only 30 ug for the nearby observer. This analysis also shows that the post exposure breath elimination of other gasoline constituents was consistent with previously published observations.

These results demonstrate that this new methodology can be used effectively in studies designed to assess exposures to MTBE. The method can be used to objectively demonstrate recent exposures, the relative magnitude of an exposure, and the approximate duration of the resulting bloodborne dose. Once a blood/breath partition coefficient for MTBE has been firmly established, the bloodborne concentration of the absorbed material can be determined using these techniques as well.     

The use of environmental metabolomics to determine glyphosate level of exposure in rapeseed (Brassica napus L.) seedlings

Metabolic profiling in plants can be used to differentiate between treatments and to search for biomarkers for exposure. A methodology for processing Ultra-High-Performance Liquid Chromatography-Diode-Array-Detection data is devised. This methodology includes a scheme for selecting informative wavelengths, baseline removal, retention time alignment, selection of relevant retention times, and principal component analysis (PCA). Plant crude extracts from rapeseed seedling exposed to sublethal concentrations of glyphosate are used as a study case. Through this approach, plants exposed to concentrations down to 5 μM could be distinguished from the controls. The compounds responsible for this differentiation were partially identified and were different from those specific for high exposure samples, which suggests that two different responses to glyphosate are elicited in rapeseed depending on the level of exposure. The PCA loadings indicate that a combination of other metabolites could be more sensitive than the response of shikimate to detect glyphosate exposure.     

The use of passive sampling to monitor forest exposure to O3, NO2 and SO2: a review and some case studies

The use of passive sampler systems is reviewed and discussed. These devices are able to determine both spatial and temporal differences in canopy exposure, as is demonstrated by their use in extensive monitoring of air-pollution exposure in forest health plots. Categorising forest health monitoring plots according to air-pollution exposure permits cause-effect analysis of certain forest health responses. In addition, passive sampling may identify areas affected by interaction between different gaseous pollutants. Passive samplers at the stand level can be used to resolve vertical profiles of ozone within the stand, and edge effects, which are important in exposure of understorey and ground flora. Recent case studies using passive samplers to determine forest exposure to gaseous pollutants indicate a potential for the development of spatial models on regional-, landscape-, and stand-level scales and the verification of atmospheric transport models.     

Computer Analysis of the California Cycle

The current trend toward greater instrumentation in analysis of automobile exhaust gases is sure to increase in future years. This will come about as a result of increased activity to find a solution to air pollution problems resulting from automobile exhaust gases. Owing to the complex driving cycle needed to evaluate vehicle emission, considerable time has been required to analyze and interpret the records from any run. To alleviate this analytic problem, an automatic data logger was obtained that records on magnetic tape the signal from a number of analytical instruments each second (in binary coded decimal form). These records are then analyzed on a computer to give integrated and averaged results over the specified driving cycle. There is no longer any need for manual evaluation of the data. In addition to the standard analyses already mentioned, the use of the data logger permits a much more complete analysis of what is happening in a vehicle. For example, the analytical instruments used include an oxygen analyzer in addition to carbon dioxide, carbon monoxide, and hydrocarbons. With these measurements the air-fuel ratio of any or all driving conditions can be calculated.     

Designing Optimal Strategies to Attain the New U.S. Particulate Matter Standards: Some Initial Concepts

ABSTRACT

This paper proposes that a fundamental principle for designing optimal strategies to attain new U.S. particulate matter (PM) standards be minimization of community and susceptible group exposure to, and inhaled dosage of, ambient PM. Properly done, such minimization maximizes human health risk reduction. To illustrate implementation of such a principle, an initial prototype model, PM Exposure (PMEX), is described that calculates PM exposure and inhaled dosage as figures-of-merit for control strategy optimization. The model accounts for age-occupation and susceptible group activity patterns, indoor-outdoor concentration differences, and geographical location. Modeling results are presented for a hypothetical example, apportioning inhaled dosage among different classes of sources, under alternative assumptions about the relative potency of different PM species categories. The results, while preliminary, demonstrate that conclusions about source class contribution based on inhaled dosage can be appreciably different than those based on ambient air measurements or emission inventories.     

Extinction risk to bird populations caused by DDT exposure

The impact of toxic chemicals on wild animals and plants can be quantified in terms of the enhanced risk of population extinction. To illustrate a method for doing this, we estimated such impact for two bird species: herring gull (Larus argentatus) in Long Island, NY, and sparrowhawk (Accipiter nisus) in eastern England, when they were exposed to DDT (p,p(')-dichlorodiphenyltrichloroethane) and its metabolites (called DDTs). The method we used is based on a formula of the mean time to population extinction derived for a stochastic differential equation (the canonical model). The intrinsic rate of natural population growth was estimated from an exponentially growing population, and the intensity of the environmental fluctuation was estimated from the magnitude of population size fluctuation. The effect of exposure to DDTs in reducing the population growth rate was evaluated based on an age-structured population model, by assuming that age-specific fertility is density-dependent and sensitive to DDTs exposure, but age-specific survivorship is not. The results are expressed in terms of the risk equivalent--the decrease in carrying capacity K that causes the same enhancement of extinction risk as chemical exposure at a given level. The risk equivalent can be used in mitigation banking.     

A comparison of indices that describe the relationship between exposure to ozone and reduction in the yield of agricultural crops

The objective of this study is to compare the use of several indices of exposure in describing the relationship between O₃ and reduction in agricultural crop yield. No attempt has been made to determine which exposure-response models best fit the data sets examined. Hourly mean O₃ concentration data, based on two-three measurements per hour, were used to develop indices of exposure from soybean and winter wheat experiments conducted in open-top chambers at the Boyce Thompson Institute, Ithaca, New York NCLAN field site. The comparative efficacy of cumulative indices (i.e. number of occurrences equal to or above specific hourly mean concentrations, sum of all hourly mean concentrations equal to or above a selected level, and the weighted sum of all hourly mean concentrations) and means calculated over an experimental period to describe the relationship between exposure to O₃ and reductions in the yield of agricultural crops was evaluated. None of the exposure indices consistently provided a best fit with the Weibull and linear models tested. The selection of the model appears to be important in determining the indices that best describe the relationship between exposure and response. The focus of selecting a model should be on fitting the data points as well as on adequately describing biological responses. The investigator should be careful to couple the model with data points derived from indices relevant to the length of exposure. While we have used a small number of data sets, our analysis indicates that exposure indices that weight peak concentrations differently than lower concentrations of an exposure regime can be used in the development of exposure-response functions. Because such indices may have merit from a regulatory perspective, we recommend that additional data sets be used in further analyses to explore the biological rationale for various indices of exposure and their use in exposure-response functions.     

A new dose model for assessment of health risk due to contaminants in air

The problem of making quantitative assessments of the risks associated with human exposure to toxic contaminants in the environment is a pressing one. This study demonstrates the capability of a new computational technique involving the use of fuzzy logic and neural networks to produce realistic risk assessments. The systematic analysis of human exposure involves the use of measurements and models, the results of which are sometimes used in regulatory decisions or in the drafting of legislation. Because of limited scientific understanding, however, interpretation of models often involves substantial uncertainty. Extensive measurement programs can be very expensive. The high complexity and inherent heterogeneity of exposure analysis is still a major challenge. The approach to this challenge tested here is to use a new model incorporating sophisticated artificial intelligence algorithms. Exposure assessment often requires that a number of factors be evaluated, including exposure concentrations, intake rates, exposure times, and frequencies. These factors are incorporated into a system that can "learn" the relevant relationships based on a known data set. The results can then be applied to new data sets and thus be applied widely without the need for extensive measurements. In this analysis, an example is developed for human health risk through inhalation exposure to benzene from vehicular emissions in the cities of Auckland and Christchurch, New Zealand. Risk factors considered were inhaled contaminant concentration, age, body weight, and activity patterns of humans. Three major variables affecting the inhaled contaminant concentration were emissions (mainly from motor vehicles), meteorology (wind speed, temperature, and atmospheric stability), and site factors (hilly, flat, etc.). The results are preliminary and used principally to demonstrate the technique, but they are very encouraging.     

Disaggregation of nation-wide dynamic population exposure estimates in The Netherlands: Applications of activity-based transport models

Traditional exposure studies that link concentrations with population data do not always take into account the temporal and spatial variations in both concentrations and population density. In this paper we present an integrated model chain for the determination of nation-wide exposure estimates that incorporates temporally and spatially resolved information about people's location and activities (obtained from an activity-based transport model) and about ambient pollutant concentrations (obtained from a dispersion model). To the best of our knowledge, it is the first time that such an integrated exercise was successfully carried out in a fully operational modus for all models under consideration. The evaluation of population level exposure in The Netherlands to NO₂ at different time-periods, locations, for different subpopulations (gender, socio-economic status) and during different activities (residential, work, transport, shopping) is chosen as a case-study to point out the new features of this methodology. Results demonstrate that, by neglecting people's travel behaviour, total average exposure to NO₂ will be underestimated by 4% and hourly exposure results can be underestimated by more than 30%. A more detailed exposure analysis reveals the intra-day variations in exposure estimates and the presence of large exposure differences between different activities (traffic > work > shopping > home) and between subpopulations (men > women, low socio-economic class > high socio-economic class). This kind of exposure analysis, disaggregated by activities or by subpopulations, per time of day, provides useful insight and information for scientific and policy purposes. It demonstrates that policy measures, aimed at reducing the overall (average) exposure concentration of the population may impact in a different way depending on the time of day or the subgroup considered. From a scientific point of view, this new approach can be used to reduce exposure misclassification.     

Evaluation of Methodologies for Exposure Assessment to Atmospheric Pollutants from a Landfill Site

Abstract

Epidemiological studies around landfill sites are limited by several factors, particularly a lack of accurate exposure assessment. Traditionally, exposure estimates are based on distance between place of residence and the landfill site. However, this measure of exposure ignores the effects that environmental factors may have upon exposure. A previous epidemiological study at a landfill site in the United Kingdom provided the basis for a case study to investigate exposure assessment methodologies that could support ongoing and future epidemiological work. Estimation of relative exposure to atmospheric pollutants near the site was refined using the Atmospheric Dispersion Modeling System (ADMS) 3.1. Annual average concentrations were calculated around the landfill site, which was modeled as an area source with a steady release rate over its entire active surface. Local meteorological and terrain data were used in the assessment. A geographical information system (GIS) was then used to link the results of the modeling to population and other data. Sensitivity studies were included to examine the variation of predicted exposure with several modeling assumptions and hence set other uncertainties in context. No simple relationship existed between the relative individual exposure measured by distance from the site and by dispersion modeling. A reassessment of exposure assessment in epidemiological studies around landfill sites was then undertaken with the refined estimates of exposure. This concluded that use of distance from the site as a proxy for exposure could lead to significant exposure misclassification in comparison with exposure assessment using atmospheric dispersion modeling and GIS. The study also indicated that assessment of peak exposure rates (i.e., extreme concentration levels) might be necessary in some epidemiological work. Optimum strategies for increasing the probability of observing effects in the more highly exposed population can be derived by combining the results of dispersion modeling with population data and, where feasible, knowledge of the toxicology of the substances of interest.