Geometric mean estimation from pooled samples

Biomonitoring for environmental chemicals presents various challenges due to the expense of measuring some compounds and the fact that in some samples the levels of many compounds may be below the limit of detection (LOD) of the measuring instrument. Even though various statistical methods have been developed to address issues associated with data being censored because results were below the LOD, the expense of measuring many compounds in large numbers of subjects remains a challenge. One solution to these challenges is to use pooled samples. There are many problems associated with the use of pooled samples as compared with individual samples, but using pooled samples can sometimes reduce the number of analytical measurements needed. Also, because pooled samples often have larger sample volumes, using pooled samples can result in lower LODs and thereby decrease the likelihood that results will be censored. However, many data sets obtained from environmental measurements have been shown to have a log-normal distribution, so using pooled samples presents a new problem: The measured value for a pooled sample is comparable to an arithmetic average of log-normal results and thus represents a biased estimate of the central tendency of the samples making up the pool. In this paper, we present a method for correcting the bias associated with using data from pooled samples with a log-normal distribution. We use simulation experiments to demonstrate how well the bias-correction method performs. We also present estimates for levels of PCB 153 and p,p'-DDE using data from pooled samples from the 2001 to 2002 National Health and Nutrition Examination Surveys.     

Development of probabilistic emission inventories of air toxics for Jacksonville, Florida, USA

Probabilistic emission inventories were developed for 1,3-butadiene, mercury (Hg), arsenic (As), benzene, formaldehyde, and lead for Jacksonville, FL. To quantify inter-unit variability in empirical emission factor data, the Maximum Likelihood Estimation (MLE) method or the Method of Matching Moments was used to fit parametric distributions. For data sets that contain nondetected measurements, a method based upon MLE was used for parameter estimation. To quantify the uncertainty in urban air toxic emission factors, parametric bootstrap simulation and empirical bootstrap simulation were applied to uncensored and censored data, respectively. The probabilistic emission inventories were developed based on the product of the uncertainties in the emission factors and in the activity factors. The uncertainties in the urban air toxics emission inventories range from as small as -25 to +30% for Hg to as large as -83 to +243% for As. The key sources of uncertainty in the emission inventory for each toxic are identified based upon sensitivity analysis. Typically, uncertainty in the inventory of a given pollutant can be attributed primarily to a small number of source categories. Priorities for improving the inventories and for refining the probabilistic analysis are discussed.     

Statistics of extreme values of air quality—a simulation study

Some of the existing National Ambient Air Quality Standards require the use of an extreme observed concentration in a year to determine compliance. Since observed extreme values tend to be not reliable, different statistical approaches for determining the extreme values have been used or suggested. However, none of these approaches properly take into account the effects of an underlying trend and the serial correlation of the air quality time series. By means of a time series simulation, these effects can be considered concurrently in estimating the extreme values.This paper reports the results of such a simulation for determining the statistics of the seven highest values (rank m = 1–7, m = 1 representing the highest value) using actual air quality data that contain both trends and autocorrelations. The result of this simulation shows that for a high-pollution season of 122 days, the commonly used asymptotic distributions overestimate the maximum (m = 1) values and underestimate their uncertainties. As one moves from m = 1 to m = 7, the over- and underestimations by the asymptotic distributions worsen (compared to the simulation result). These findings in logarithmic space are further enhanced when they are converted back to concentration space. The simulation using the oxidant data for Azusa, California further shows that the uncertainties associated with the estimates Of the extreme values are typically 20% of the values for m = 1 and 10% of the values for m = 7. Compared to the observed data, which is a single series for each year, the result based on the popular lognormal distribution consistently overpredicts the extreme values, by about 40% for the maximum values and about 20% for the seventh highest values.Our results illustrate the difficulty of estimating the extreme values of air quality time series with accuracy and confidence. However, the accuracy and confidence of the estimates improve as the rank moves away from the extreme. This result calls for the need for using a less extreme value in setting a sensible air quality standard. Of course, such a standard can be set without sacrificing its stringency.     

Development of Probabilistic Emission Inventories of Air Toxics for Jacksonville,Florida

Abstract

Probabilistic emission inventories were developed for 1,3-butadiene, mercury (Hg), arsenic (As), benzene, formaldehyde, and lead for Jacksonville, FL. To quantify inter-unit variability in empirical emission factor data, the Maximum Likelihood Estimation (MLE) method or the Method of Matching Moments was used to fit parametric distributions. For data sets that contain nondetected measurements, a method based upon MLE was used for parameter estimation. To quantify the uncertainty in urban air toxic emission factors, parametric bootstrap simulation and empirical bootstrap simulation were applied to uncensored and censored data, respectively. The probabilistic emission inventories were developed based on the product of the uncertainties in the emission factors and in the activity factors. The uncertainties in the urban air toxics emission inventories range from as small as –25 to +30% for Hg to as large as –83 to +243% for As. The key sources of uncertainty in the emission inventory for each toxic are identified based upon sensitivity analysis. Typically, uncertainty in the inventory of a given pollutant can be attributed primarily to a small number of source categories. Priorities for improving the inventories and for refining the probabilistic analysis are discussed.     

Confidence limits for air quality model evaluations,as estimated by bootstrap and jackknife resampling methods

Air quality models are used to make decisions regarding the construction of industrial plants, the types of fuel that will be burnt and the types of pollution control devices that will be used. It is important to know the uncertainties that are associated with these model predictions. Standard analytical methods found in elementary statistics textbooks for estimating uncertainties are generally not applicable since the distributions of performance measures related to air quality concentrations are not easily transformed to a Gaussian shape. This paper suggests several possible resampling procedures that can be used to calculate uncertainties or confidence limits on air quality model performance. In these resampling methods, many new data sets are drawn from the original data set using an empirical set of rules. A few alternate forms of the socalled bootstrap and jackknife resampling procedures are tested using a concocted data set with a Gaussian parent distributions, with the result that the jackknife is the most efficient procedure to apply, although its confidence bounds are slightly overestimated. The resampling procedures are then applied to predictions by seven air quality models for the Carpinteria coastal dispersion experiment. Confidence intervals on the fractional mean bias and the normalized mean square error are calculated for each model and for differences between models. It is concluded that these uncertainties are sometimes so large for data sets consisting of about 20 elements that it cannot be stated with 95% confidence that the performance measure for the ‘best’ model is significantly different from that for another model.     

Quantification of emission factor uncertainty

Emissions factors are important for estimating and characterizing emissions from sources of air pollution. There is no quantitative indication of uncertainty for these emission factors, most factors do not have an adequate data set to compute uncertainty, and it is very difficult to locate the data for those that do. The objectives are to compare the current emission factors of Electric Generating Unit NOx sources with currently available continuous emission monitoring data, develop quantitative uncertainty indicators for the Environmental Protection Agency (EPA) data quality rated emission factors, and determine the possible ranges of uncertainty associated with EPA's data quality rating of emission factors. EPA's data letter rating represents a general indication of the robustness of the emission factor and is assigned based on the estimated reliability of the tests used to develop the factor and on the quantity and representativeness of the data. Different sources and pollutants that have the same robustness in the measured emission factor and in the representativeness of the measured values are assumed to have a similar quantifiable uncertainty. For the purposes of comparison, we assume that the emission factor estimates from source categories with the same letter rating have enough robustness and consistency that we can quantify the uncertainty of these common emission factors based on the qualitative indication of data quality which is known for almost all factors. The results showed that EPA's current emission factor values for NOx emissions from combustion sources were found to be reasonably representative for some sources; however AP-42 values should be updated for over half of the sources to reflect current data. The quantified uncertainty ranges were found to be 25-62% for A rated emission factors, 45-75% for B rated emission factors, 60-82% for C rated emission factors, and 69-86% for D rated emission factors, and 82-92% for E rated emission factors.     

Estimation of measurement uncertainty associated to the determination of pesticide residues: a case study

In the analytical analysis the measurement uncertainty is a quantitative indicator of the confidence describing the range around a reported or experimental result within which the true value can be expected. Several approaches can be used to estimate the measurement uncertainty associated to the analysis of pesticide residues: a) the top-down, the estimation can be referred to default values; b) the bottom-up the estimation is related to the uncertainty sources. Concerning the bottom-up approach, the following contributions have been investigated: weight of sample, calibration solutions, final volume of sample and intermediate repeatability studies. The commodity/residue combination selected in this study was celery/tau-fluvalinate pesticide. Tau-fluvalinate is a broad-spectrum insecticide in the pyrethroid class of pesticides. The Maximum Residue Limit (MRL) of tau-fluvalinate in celery has been set at 0.01 mg/kg. The tau- Fluvalinate showed two chromatographic peaks. Since the individual standards are not available, the two peaks were integrated separately and the instrumental responses were added. The total residue was calculated on the basis of resulted peaks. The present work aims to compare the uncertainty estimated by experimental data using repeated analysis (n = 12) of a real sample and a spiked sample. The relative expanded uncertainty for two data set, incurred and spiked, was 22 % and 20 %, respectively. No differences were observed from repeated determinations of real samples and spiked samples.     

Assigning concentration values for dioxin and furan congeners in human serum when measurements are below limits of detection: an observational approach

Concentrations of polychlorinated dibenzo-p-dioxins (PCDDs) and dibenzofurans (PCDFs) in dietary sources for humans have been declining over the previous two decades. These declines have been accompanied by decreases in concentrations of these compounds in humans, as evidenced by measurements in blood and milk. Because of the decreasing concentrations of PCDD/PCDFs in the environment and in humans, measuring PCDD/PCDF congeners in humans has become increasingly difficult, despite advances in analytic methods. An observational approach was recently described to address the quandary of non-detectable results in determining toxic equivalents. This approach, called the congener ratio approach, is specifically for cases where concentrations of 2,3,7,8-TCDD (TCDD) are below the limit of detection (LOD), and where concentrations of 1,2,3,7,8-pentachlorodibenzo-p-dioxin (PeCDD) are equal to or above the LOD. Development of this approach relied on evaluating data on measured concentrations of TCDD and PeCDD in human serum from the general population. The congener ratio approach for TCDD and PeCDD was based on the concentration of TCDD being approximately 40% that of PeCDD in serum from the general population. Additional analyses presented here reveal that when concentrations of both congeners are above the LOD, the data appear to generally support the congener ratio approach for TCDD and PeCDD, with the caveat that gender may affect the ratio. However, the TCDD/PeCDD relation is less clear when TCDD is less than the LOD; in this situation, the relation overpredicts levels of TCDD approximately 80% of the time for the 2001-2002 NHANES database. Using the congener ratio approach for other PCDD/PCDF congeners requires assessing the correlation and the frequency of detection for both TCDD and PeCDD.     

Percentile estimation using variable censored data

Much progress has been made in recent years to address the estimation of summary statistics, using data that are subject to censoring of results that fall below the limit of detection (LOD) for the measuring instrument. Truncated data methods (e.g., Tobit regression) and multiple-imputation are two approaches for analyzing data results that are below the LOD. To apply these methods requires an assumption about the underlying distribution of the data. Because the log-normal distribution has been shown to fit many data sets obtained from environmental measurements, the common practice is to assume that measurements of environmental factors can be described by log-normal distributions. This article describes methods for obtaining estimates of percentiles and their associated confidence intervals when the results are log-normal and a fraction of the results are below the LOD. We present limited simulations to demonstrate the bias of the proposed estimates and the coverage probability of their associated confidence intervals. Estimation methods are used to generate summary statistics for 2,3,7,8-tetrachloro dibenzo-p-dioxin (2,3,7,8-TCDD) using data from a 2001 background exposure study in which PCDDs/PCDFs/cPCBs in human blood serum were measured in a Louisiana population. Because the congener measurements used in this study were subject to variable LODs, we also present simulation results to demonstrate the effect of variable LODs on the multiple-imputation process.     

A real-time monitoring and assessment method for calculation of total amounts of indoor air pollutants emitted in subway stations

Subway systems are considered as main public transportation facility in developed countries. Time spent by people in indoors, such as underground spaces, subway stations, and indoor buildings, has gradually increased in the recent past. Especially, operators or old persons who stay in indoor environments more than 15 hr per day usually influenced a greater extent by indoor air pollutants. Hence, regulations on indoor air pollutants are needed to ensure good health of people. Therefore, in this study, a new cumulative calculation method for the estimation of total amounts of indoor air pollutants emitted inside the subway station is proposed by taking cumulative amounts of indoor air pollutants based on integration concept. Minimum concentration of individual air pollutants which naturally exist in indoor space is referred as base concentration of air pollutants and can be found from the data collected. After subtracting the value of base concentration from data point of each data set of indoor air pollutant, the primary quantity of emitted air pollutant is calculated. After integration is carried out with these values, adding the base concentration to the integration quantity gives the total amount of indoor air pollutant emitted. Moreover the values of new index for cumulative indoor air quality obtained for 1 day are calculated using the values of cumulative air quality index (CAI). Cumulative comprehensive indoor air quality index (CCIAI) is also proposed to compare the values of cumulative concentrations of indoor air pollutants. From the results, it is clear that the cumulative assessment approach of indoor air quality (IAQ) is useful for monitoring the values of total amounts of indoor air pollutants emitted, in case of exposure to indoor air pollutants for a long time. Also, the values of CCIAI are influenced more by the values of concentration of NO2, which is released due to the use of air conditioners and combustion of the fuel. The results obtained in this study confirm that the proposed method can be applied to monitor total amounts of indoor air pollutants emitted, inside apartments and hospitals as well. Implications: Nowadays, subway systems are considered as main public transportation facility in developed countries. Time spent by people in indoors, such as underground spaces, subway stations, and indoor buildings, has gradually increased in the recent past. Especially, operators or old persons who stay in the indoor environments more than 15 hr per day usually influenced a greater extent by indoor air pollutants. Hence, regulations on indoor air pollutants are needed to ensure good health of people. Therefore, this paper presents a new methodology for monitoring and assessing total amounts of indoor air pollutants emitted inside underground spaces and subway stations. A new methodology for the calculation of cumulative amounts of indoor air pollutants based on integration concept is proposed. The results suggest that the cumulative assessment approach of IAQ is useful for monitoring the values of total amounts of indoor air pollutants, if indoor air pollutants accumulated for a long time, especially NO2 pollutants. The results obtained here confirm that the proposed method can be applied to monitor total amounts of indoor air pollutants emitted, inside apartments and hospitals as well.     

An information theoretic perspective on mesoscale seasonal variations in ground-level ozone

Relatively little prior use has been made of information theory in air quality analysis. This paper explores whether basic, but formal, quantitative measures of information content might yield fresh perspectives on seasonal variations in the ground-level ozone concentration field across the lower Fraser Valley (LFV), British Columbia, Canada. I calculate Shannon entropy in daily maximum ozone concentration on a month-by-month, station-by-station basis, using 1 year of hourly measurements from 18 air quality monitoring stations. The values are then qualitatively compared with an eye to identifying spatial and seasonal patterns. The results further demonstrate the potential utility of information theoretic concepts for assessing air quality variability; yield some new insight into tropospheric ozone dynamics across the LFV; and may provide some guidance to the refinement of monitoring network configuration. Of particular note is that, although entropy and mean concentration exhibit some similarities in their respective seasonal patterns, maximum uncertainty and information content appears to occur at times and locations somewhat different from those at which highest concentrations are experienced.     

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.     

On variability of atmospheric co2 at station Alert

Weekly measurements of atmospheric CO₂ concentration have been performed at Alert, Canada for the last 10 y or so. From this data set a subset, composed of measurements taken during a summer season and a winter season, was chosen to investigate if there is a consistent relationship between anomalous CO₂ concentration values and trajectories of air parcels arriving at Alert.In agreement with earlier studies of Pt. Barrow data, relative CO₂ maxima in the summer season were associated with air parcels arriving at Alert from tundra and the U.S.S.R. Air parcels from southern regions were associated with relative minima. In the winter season, relative maxima were associated with air parcels from northern Europe and the U.S.S.R., while relative minima were associated with those air parcels from southern latitudes.     

Modeling distributions of air pollutant concentrations—II. Estimation of one and two parameter statistical distributions

In Part I of this series Taylor, Jakeman and Simpson (1986, Atmospheric Environment, 20, 1781–1789) examined the problem of identifying the appropriate distributional form for air pollution concentration data. In this paper we examine the parameter estimation problem. Monte Carlo simulation is used to compare methods for fitting statistical distributions to such data where the distributional form is known. Three methods are investigated for estimating the parameters of the lognormal distribution, two methods for the exponential distribution, three methods for the γ-distribution and four methods for the Weibull distribution. For all distributions and for each method we examine the accuracy with which the upper percentiles of the distribution are evaluated as it is these percentiles which are referred to by air quality standards. For each distribution a simple empirical model, which yields approximations to the relative root mean square error of the percentile estimates against sample size and parameter values, is developed and demonstrated. Thus for each distributional model an estimate of the relative error associated with evaluating high pollutant levels may be readily determined.     

A real-time monitoring and assessment method for calculation of total amounts of indoor air pollutants emitted in subway stations

Subway systems are considered as main public transportation facility in developed countries. Time spent by people in indoors, such as underground spaces, subway stations, and indoor buildings, has gradually increased in the recent past. Especially, operators or old persons who stay in indoor environments more than 15 hr per day usually influenced a greater extent by indoor air pollutants. Hence, regulations on indoor air pollutants are needed to ensure good health of people. Therefore, in this study, a new cumulative calculation method for the estimation of total amounts of indoor air pollutants emitted inside the subway station is proposed by taking cumulative amounts of indoor air pollutants based on integration concept. Minimum concentration of individual air pollutants which naturally exist in indoor space is referred as base concentration of air pollutants and can be found from the data collected. After subtracting the value of base concentration from data point of each data set of indoor air pollutant, the primary quantity of emitted air pollutant is calculated. After integration is carried out with these values, adding the base concentration to the integration quantity gives the total amount of indoor air pollutant emitted. Moreover, the values of new index for cumulative indoor air quality obtained for 1 day are calculated using the values of cumulative air quality index (CAI). Cumulative comprehensive indoor air quality index (CCIAI) is also proposed to compare the values of cumulative concentrations of indoor air pollutants. From the results, it is clear that the cumulative assessment approach of indoor air quality (IAQ) is useful for monitoring the values of total amounts of indoor air pollutants emitted, in case of exposure to indoor air pollutants for a long time. Also, the values of CCIAI are influenced more by the values of concentration of NO₂, which is released due to the use of air conditioners and combustion of the fuel. The results obtained in this study confirm that the proposed method can be applied to monitor total amounts of indoor air pollutants emitted, inside apartments and hospitals as well.

Implications: Nowadays, subway systems are considered as main public transportation facility in developed countries. Time spent by people in indoors, such as underground spaces, subway stations, and indoor buildings, has gradually increased in the recent past. Especially, operators or old persons who stay in the indoor environments more than 15 hr per day usually influenced a greater extent by indoor air pollutants. Hence, regulations on indoor air pollutants are needed to ensure good health of people. Therefore, this paper presents a new methodology for monitoring and assessing total amounts of indoor air pollutants emitted inside underground spaces and subway stations. A new methodology for the calculation of cumulative amounts of indoor air pollutants based on integration concept is proposed. The results suggest that the cumulative assessment approach of IAQ is useful for monitoring the values of total amounts of indoor air pollutants, if indoor air pollutants accumulated for a long time, especially NO₂ pollutants. The results obtained here confirm that the proposed method can be applied to monitor total amounts of indoor air pollutants emitted, inside apartments and hospitals as well.     

Update of daily intake of PCDDs, PCDFs, and dioxin-like PCBs from food in Japan

Total diet study (TDS) samples of 14 food groups from 16 locations in Japan, collected in 1999 and 2000, were analyzed for polychlorinated dibenzo-p-dioxins (PCDDs), dibenzofurans (PCDFs), and dioxin-like polychlorinated biphenyls (dioxin-like PCBs) to estimate the update of daily intake of these contaminants from food. The mean daily intake of toxic equivalency (TEQ) for an adult weighing 50 kg, calculated at non-detected isomer concentrations equal to zero (ND=0), was estimated to be 2.25 pg TEQ/kg b.w./day. When non-detected isomer concentrations are assumed to be equal to half of the limits of detection (ND=1/2 LOD), the mean daily intake was estimated to be 3.22 pg TEQ/kg b.w./day. These values were below the tolerable daily intake (TDI) of 4 pg TEQ/kg b.w. for PCDD/Fs and dioxin-like PCBs set in Japan. In both the estimates, the mean daily intakes were highest from fish and shellfish (76.9% at ND=0 and 53.9% at ND=1/2 LOD of the total TEQs), followed by those from meat and eggs (15.5% at ND=0 and 11.7% at ND=1/2 LOD of the total TEQs). Congener specific data revealed that these total TEQ levels were dominated by 1,2,3,7,8-PeCDD, 2,3,4,7,8-PeCDF and 3,3^′,4,4^′,5-PeCB in each case (71.7% at ND=0 and 63.1% at ND=1/2 LOD of the total TEQs). The dioxin-like PCBs (non-ortho and mono-ortho PCBs) accounted for about 50% of these total TEQs. These data will be very useful in the risk assessment of PCDD/Fs and dioxin-like PCBs from food in Japan.     

Interlaboratory comparison of source apportionment procedures: Results for simulated data sets

Three sets of simulated aerosol compositional data were prepared to (1) assess the current state of the art of source apportionment procedures and (2) provide initial sets of test data to aid method development. The data sets were generated from reported source profiles, real meteorological data (St. Louis, 1976) and two constructed city plans. Following plume dispersion by means of the RAM model, 40 ‘samples’ having known source contributions and error structure were generated for each set. Seven laboratories participating in the Mathematical and Empirical Receptor Models Workshop (Quail Roost II) undertook deconvolution of one or two of the sets by various numerical techniques (chemical mass balance or multivariate). Comparison of the participants' results with the known source contributions showed that the source contribution estimates were consistent with the truth within a factor of ~ 2 and that the uncertainty estimates ranged from much too conservative (broad) to much too small. No unique method of choice emerged from this exercise; the participants' various techniques appeared complementary and capable of resolving ~ 6–9 different sources. The intercomparison did allow us to formulate suggestions for improving the simulation process per se and for improving the various treatments (especially with respect to estimating uncertainties).     

Relating meteorological variables and trends in motor vehicle emissions to monthly urban carbon monoxide concentrations

A model which quantifies the relationship between the monthly time series for CO emissions, the monthly time series in ambient CO concentration, and meteorologically driven dispersion was developed. Fifteen cities representing a wide range of geographical and climatic conditions were selected. An eight-year time series (1984–1991 inclusive) of monthly averaged data were examined in each city. A new method of handling missing ambient concentration values which is designed to calculate city-wide average concentrations that follow the trend seen at individual monitor sites is presented. This method is general and can be used in other applications involving missing data. The model uses emissions estimates along with two meteorological variables (wind speed and mixing height) to estimate monthly averages of ambient air pollution concentrations. The model is shown to have a wide range of applicability; it works equally well for a wide range of cities that have very different temporal CO distributions. The model is suited for assessing long-term trends in ambient air pollutants and can also be used for estimating seasonal variations in concentration, estimation of trends in emissions, and for filling in gaps in the ambient concentration record.     

A sensitivity analysis of adsorption and degradation parameters in the modeling of pesticide transport in soils

The increasing use of deterministic models in predicting the movement of pesticides in soils, has focused attention on the evaluation of major parameters which represent attenuation factors of organics in the subsurface. These parameters are the degradation rate constant and the adsorption constant for the pesticide. In view of the large in situ variability of these parameters and of the difficulty in obtaining accurate field data, there is a high degree of uncertainty associated with the results obtained from deterministic models. A sensitivity analysis is performed here to quantify the impact of such variation in each of these input parameters on the output results of an unsaturated zone transport model (PRZM). Results show that variations in these parameters about their respective mean values greatly affect the predicted concentration distributions, obtained after three years, of the pesticide aldicarb in all the soil profile. A 15–22% variation in the degradation constant, or a 24% variation in the adsorption constant, lead to a 100% uncertainty in the various simulation results defined as the cumulative quantity of aldicarb or the dissolved aldicarb concentration leached below the root zone (or the unsaturated zone) of the soil. Such a deterministic model presents a high degree of sensitivity to these input parameters. Accurate field data are then needed to obtain reliable model results in predicting pesticide movement inthe unsaturated zone.     

The Use of Indoor Air Measurements To Evaluate Intrusion of Subsurface VOC Vapors into Buildings

ABSTRACT

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.