Wayne R. Ott

The National Human Activity Pattern Survey (NHAPS): A resource for assessing exposure to environmental pollutants

Because human activities impact the timing, location, and degree of pollutant exposure, they play a key role in explaining exposure variation. This fact has motivated the collection of activity pattern data for their specific use in exposure assessments. The largest of these recent efforts is the National Human Activity Pattern Survey (NHAPS), a 2-year probability-based telephone survey ( n=9386) of exposure-related human activities in the United States (U.S.) sponsored by the U.S. Environmental Protection Agency (EPA). The primary purpose of NHAPS was to provide comprehensive and current exposure information over broad geographical and temporal scales, particularly for use in probabilistic population exposure models. NHAPS was conducted on a virtually daily basis from late September 1992 through September 1994 by the University of Marylands Survey Research Center using a computer-assisted telephone interview instrument (CATI) to collect 24-h retrospective diaries and answers to a number of personal and exposure-related questions from each respondent. The resulting diary records contain beginning and ending times for each distinct combination of location and activity occurring on the diary day (i.e., each microenvironment). Between 340 and 1713 respondents of all ages were interviewed in each of the 10 EPA regions across the 48 contiguous states. Interviews were completed in 63% of the households contacted. NHAPS respondents reported spending an average of 87% of their time in enclosed buildings and about 6% of their time in enclosed vehicles. These proportions are fairly constant across the various regions of the U.S. and Canada and for the California population between the late 1980s, when the California Air Resources Board (CARB) sponsored a state-wide activity pattern study, and the mid-1990s, when NHAPS was conducted. However, the number of people exposed to environmental tobacco smoke (ETS) in California seems to have decreased over the same time period, where exposure is determined by the reported time spent with a smoker. In both California and the entire nation, the most time spent exposed to ETS was reported to take place in residential locations.

A Physical Explanation of the Lognormality of Pollutant Concentrations

Investigators in different environmental fields have reported that the concentrations of various measured substances have frequency distributions that are lognormal, or nearly so. That is, when the logarithms of the observed concentrations are plotted as a frequency distribution, the resulting distribution is approximately normal, or Gaussian, over much of the observed range. Examples include radionuclides in soil, pollutants in ambient air, indoor air quality, trace metals in streams, metals in biological tissue, calcium in human remains. The ubiquity of the lognormal distribution in environmental processes is surprising and has not been adequately explained, since common processes in nature (for example, computation of the mean and the analysis of error) usually give rise to distributions that are normal rather than lognormal. This paper takes the first step toward explaining why lognormal distributions can arise naturally from certain physical processes that are analogous to those found in the environment. In this paper, these processes are treated mathematically, and the results are illustrated in a laboratory beaker experiment that is simulated on the computer.

Real–Time Measurement of Outdoor Tobacco Smoke Particles

Abstract The current lack of empirical data on outdoor tobacco smoke (OTS) levels impedes OTS exposure and risk assessments. We sought to measure peak and time-averaged OTS concentrations in common outdoor settings near smokers and to explore the determinants of time-varying OTS levels, including the effects of source proximity and wind. Using five types of real-time airborne particle monitoring devices, we obtained more than 8000 min worth of continuous monitoring data, during which there were measurable OTS levels. Measurement intervals ranged from 2 sec to 1 min for the different instruments. We monitored OTS levels during 15 on-site visits to 10 outdoor public places where active cigar and cigarette smokers were present, including parks, sidewalk cafés, and restaurant and pub patios. For three of the visits and during 4 additional days of monitoring outdoors and indoors at a private residence, we controlled smoking activity at precise distances from monitored positions. The overall average OTS respirable particle concentration for the surveys of public places during smoking was approximately 30 μg m−3. OTS exhibited sharp spikes in particle mass concentration during smoking that sometimes exceeded 1000 μg m−3 at distances within 0.5 m of the source. Some average concentrations over the duration of a cigarette and within 0.5 m exceeded 200 μg m−3, with some average downwind levels exceeding 500 μg m−3. OTS levels in a constant upwind direction from an active cigarette source were nearly zero. OTS levels also approached zero at distances greater than approximately 2 m from a single cigarette. During periods of active smoking, peak and average OTS levels near smokers rivaled indoor tobacco smoke concentrations. However, OTS levels dropped almost instantly after smoking activity ceased. Based on our results, it is possible for OTS to present a nuisance or hazard under certain conditions of wind and smoker proximity.

Personal exposure to ultrafine particles.

Personal exposure to ultrafine particles (UFP) can occur while people are cooking, driving, smoking, operating small appliances such as hair dryers, or eating out in restaurants. These exposures can often be higher than outdoor concentrations. For 3 years, portable monitors were employed in homes, cars, and restaurants. More than 300 measurement periods in several homes were documented, along with 25 h of driving two cars, and 22 visits to restaurants. Cooking on gas or electric stoves and electric toaster ovens was a major source of UFP, with peak personal exposures often exceeding 100,000 particles/cm3 and estimated emission rates in the neighborhood of 1012 particles/min. Other common sources of high UFP exposures were cigarettes, a vented gas clothes dryer, an air popcorn popper, candles, an electric mixer, a toaster, a hair dryer, a curling iron, and a steam iron. Relatively low indoor UFP emissions were noted for a fireplace, several space heaters, and a laser printer. Driving resulted in moderate exposures averaging about 30,000 particles/cm3 in each of two cars driven on 17 trips on major highways on the East and West Coasts. Most of the restaurants visited maintained consistently high levels of 50,000–200,000 particles/cm3 for the entire length of the meal. The indoor/outdoor ratios of size-resolved UFP were much lower than for PM2.5 or PM10, suggesting that outdoor UFP have difficulty in penetrating a home. This in turn implies that outdoor concentrations of UFP have only a moderate effect on personal exposures if indoor sources are present. A time-weighted scenario suggests that for typical suburban nonsmoker lifestyles, indoor sources provide about 47% and outdoor sources about 36% of total daily UFP exposure and in-vehicle exposures add the remainder (17%). However, the effect of one smoker in the home results in an overwhelming increase in the importance of indoor sources (77% of the total).

Concepts of human exposure to air pollution

Abstract In recent years, considerable attention has focused on the concept of “human exposure” to environmental pollutants, but different investigators seem to have developed different definitions of this concept and used different approaches for estimating it. This paper reviews a number of “exposure” studies in a single environmental medium—air pollution—to see how others have defined this concept in the literature. Many previous investigators unfortunately calculate “exposures” by relying on data from fixed air monitoring stations, and they assume that people are located in the same place, usually their residential address, throughout a 24-h period. However, a second body of literature shows that fixed air monitoring stations do not necessarily reflect human exposures, because concentrations observed indoors—in homes, offices, factories, and motor vehicles—differ from those observed at fixed stations, and people usually spend considerable time in these locations. In an effort to standardize the nomenclature dealing with exposures, a definition is proposed in which the pollutant must come into contact with the physical boundary of the person. Then, exposure of person i to pollutant concentration c is viewed as two events occurring jointly: person i is present at a particular location, and concentration c is present at the same location. Mathematical definitions for “integrated exposure,” “average exposure,” and “standardized exposure” with various averaging periods also are introduced. Finally, two different yet compatible research approaches are suggested for determining human exposures to air pollution.

Air change rates of motor vehicles and in-vehicle pollutant concentrations from secondhand smoke

The air change rates of motor vehicles are relevant to the sheltering effect from air pollutants entering from outside a vehicle and also to the interior concentrations from any sources inside its passenger compartment. We made more than 100 air change rate measurements on four motor vehicles under moving and stationary conditions; we also measured the carbon monoxide (CO) and fine particle (PM2.5) decay rates from 14 cigarettes smoked inside the vehicle. With the vehicle stationary and the fan off, the ventilation rate in air changes per hour (ACH) was less than 1 h−1 with the windows closed and increased to 6.5 h−1 with one window fully opened. The vehicle speed, window position, ventilation system, and air conditioner setting was found to affect the ACH. For closed windows and passive ventilation (fan off and no recirculation), the ACH was linearly related to the vehicle speed over the range from 15 to 72 mph (25 to 116 km h−1). With a vehicle moving, windows closed, and the ventilation system off (or the air conditioner set to AC Max), the ACH was less than 6.6 h−1 for speeds ranging from 20 to 72 mph (32 to 116 km h−1). Opening a single window by 3″ (7.6 cm) increased the ACH by 8–16 times. For the 14 cigarettes smoked in vehicles, the deposition rate k and the air change rate a were correlated, following the equation k=1.3a (R2=82%; n=14). With recirculation on (or AC Max) and closed windows, the interior PM2.5 concentration exceeded 2000 μg m−3 momentarily for all cigarettes tested, regardless of speed. The concentration time series measured inside the vehicle followed the mathematical solutions of the indoor mass balance model, and the 24-h average personal exposure to PM2.5 could exceed 35 μg m−3 for just two cigarettes smoked inside the vehicle.

The effect of opening windows on air change rates in two homes.

Abstract More than 300 air change rate experiments were completed in two occupied residences: a two-story detached house in Redwood City, CA, and a three-story townhouse in Reston, VA. A continuous monitor was used to measure the decay of SF6 tracer gas over periods of 1-18 hr. Each experiment first included a measurement of the air change rate with all exterior doors and windows closed (State 0), then a measurement with the single change from State 0 conditions of opening one or more windows. The overall average State 0 air change rate was 0.37 air changes per hour (hr-1) (SD = 0.10 hr-1; n = 112) for the California house and 0.41 hr-1 (SD = 0.19 hr-1; n = 203) for the Virginia house. Indoor/outdoor temperature differences appeared to be responsible for the variation at the Virginia house of 0.15-0.85 hr-1 when windows were closed. Opening a single window increased the State 0 air change rate by an amount roughly proportional to the width of the opening, reaching increments as high as 0.80 hr-1 in the California house and 1.3 hr-1 in the Virginia house. Multiple window openings increased the air change rate by amounts ranging from 0.10 to 2.8 hr-1 in the California house and from 0.49 to 1.7 hr-1 in the Virginia house. Compared with temperature differences and wind effects, opening windows produced the greatest increase in the air change rates measured in both homes. Results of this study indicate the importance of occupant window-opening behavior on a home’s air change rate and the consequent need to incorporate this factor when estimating human exposure to indoor air pollutants.

VALIDATION OF THE SIMULATION OF HUMAN ACTIVITY AND POLLUTANT EXPOSURE (SHAPE) MODEL USING PAIRED DAYS FROM THE DENVER, CO, CARBON MONOXIDE FIELD STUDY

Abstract The U.S. Environmental Protection Agency (EPA) developed the Simulation of Human Activity and Pollutant Exposure (SHAPE) model to estimate the frequency distribution of population exposures to carbon monoxide (CO) by computer simulation of microenvironmental concentrations and human activity patterns. To validate the SHAPE model, measured personal CO exposures from an EPA study in Denver, CO, in the winter of 1982–83 were compared with estimates generated by the model. Microenvironmental CO concentrations for the model were generated by Monte Carlo simulation based on the Denver, microenvironmental data, but the activity simulation portions of the model were modified to accommodate real activity patterns from Denver. Observed and predicted population exposure frequency distributions then were compared. A total of 899 24-h responses from Denver yielded 772 usable profiles after invalid responses were eliminated, giving 336 paired days of observations (CO exposure profiles from two successive days for the same respondent). From these data, 22 microenvironments were identified with at least 10 measurements on each of the two days. Microenvironmental CO concentrations were calculated by subtracting hourly ambient background CO concentrations. Ambient background CO concentrations were estimated by three different approaches. All three yielded similar results, with the average value from all fixed monitoring sites performing slightly better than the nearest fixed monitoring site. For nearly every microenvironment, the study found negligible differences between the microenvironmental CO frequency distributions on the 2 days. The microenvironmental CO frequency distributions for Day 1 provided the basis for SHAPE model estimates of Day 2 exposure profiles, and the activity patterns were based on the Denver diaries for Day 2 (the observed times at which people entered and left each microenvironment). CO exposure profiles were calculated using Monte Carlo sampling from the Day 1 microenvironmental CO concentration distributions and adding the estimated ambient background components. The predicted frequency distributions of the 1- and 8-h maximum average CO concentrations agreed reasonably well with the observed frequency distributions. Mean values were quite similar, but the variability in the observed values exceeded the variability in the predicted values, which may be attributable to serial dependencies in a persons activities during a 24-h period and autocorrelation of microenvironmental concentrations and the finite nature of the distributions from which microenvironmental concentrations were sampled.

Predicting Particulate (PM10) Personal Exposure Distributions Using a Random Component Superposition Statistical Model

ABSTRACT This paper presents a new statistical model designed to extend our understanding from prior personal exposure field measurements of urban populations to other cities where ambient monitoring data, but no personal exposure measurements, exist. The model partitions personal exposure into two distinct components: ambient concentration and nonambient concentration. It is assumed the ambient and nonambient concentration components are uncorrelated and add together; therefore, the model is called a random component superposition (RCS) model. The 24-hr ambient outdoor concentration is multiplied by a dimensionless “attenuation factor” between 0 and 1 to account for deposition of particles as the ambient air infiltrates indoors. The RCS model is applied to field PM10 measurement data from three large-scale personal exposure field studies: THEES (Total Human Environmental Exposure Study) in Phillipsburg, NJ; PTEAM (Particle Total Exposure Assessment Methodology) in Riverside, CA; and the Ethyl Corporation study in Toronto, Canada. Because indoor sources and activities (smoking, cooking, cleaning, the personal cloud, etc.) may be similar in similar populations, it was hypothesized that the statistical distribution of nonambient personal exposure is invariant across cities.

Investigations of the proximity effect for pollutants in the indoor environment.

More than a dozen indoor air quality studies have reported a large discrepancy between concentrations measured by stationary indoor monitors (SIMs) and personal exposure monitors (PEMs). One possible cause of this discrepancy is a source proximity effect, in which pollutant sources close to the respondent cause elevated and highly variable exposures. This paper describes three sets of experiments in a home using real-time measurements to characterize and quantify the proximity effect relative to a fixed distant location analogous to a SIM. In the first set of experiments, using sulfur hexafluoride (SF6) as a continuously emitting tracer pollutant from a point source, measurements of pollutant concentrations were made at different distances from the source under different air exchange rates and source strengths. A second set of experiments used a continuous point source of carbon monoxide (CO) tracer pollutant and an array of high time resolution monitors to collect simultaneous concentration readings at different locations in the room. A third set of experiments measured particle count density and particle-bound polycyclic aromatic hydrocarbon (PAH) concentrations emitted from a continuous particle point source (an incense stick) using two particle counters and two PAH monitors, and included human activity periods both before and during the source emission period. Results from the SF6 and CO experiments show that while the source is emitting, a source proximity effect can be seen in the increases in the mean and median and in the variability of concentrations closest to the source, even at a distance of 2.0 m from the source under certain settings of air exchange rate and source strength. CO concentrations at locations near the source were found to be higher and more variable than the predictions of the mass balance model. For particles emitted from the incense source, a source proximity effect was evident for the fine particle sizes (0.3 to 2.5 µm) and particle-bound PAH up to at least 1.0 m from the source. Analysis of spatial and temporal patterns in the data for the three tracer pollutants reveal marked transient elevations of concentrations as seen by the monitor, referred to as “microplumes,” particularly at locations close to the source. Mixing patterns in the room show complex patterns and directional effects, as evidenced by the variable intensity of the microplume activity at different locations. By characterizing the spatial and temporal variability of pollutant concentrations in the home, the proximity effect can be quantified, leading to improved indoor monitoring designs and models of human exposure to air pollutants.