Responses to the region 5 NHEXAS time/activity diary. National Human Exposure Assessment Survey

Beyond air pollution at home: Assessment of personal exposure to PM2.5 using activity-based travel demand model and low-cost air sensor network data

Assessing personal exposure to air pollution is challenging due to the limited availability of human movement data and the complexity of modeling air pollution at high spatiotemporal resolution. Most health studies rely on residential estimates of outdoor air pollution instead which introduces exposure measurement error. Personal exposure for 100,784 individuals in Los Angeles County was estimated by integrating human movement data simulated from the Southern California Association of Governments (SCAG) activity-based travel demand model with hourly PM_2.5 predictions from my 500 m gridded model incorporating low-cost sensor monitoring data. Individual exposures were assigned considering PM_2.5 levels at homes, workplaces, and other activity locations. These dynamic exposures were compared to the residence-based exposures, which do not consider human movement, to examine the degree of exposure estimation bias. The results suggest that exposures were underestimated by 13% (range 5-22%) on average when human movement was not considered, and much of the error was eliminated by accounting for work location. Exposure estimation bias increased for people who exhibited higher mobility levels, especially for workers with long commute distances. Overall, the personal exposures of workers were underestimated by 22% (5-61%) relative to their residence-based exposures. For workers who commute >20 miles, their exposure levels can be at most underestimated by 61%. Omitting mobility resulted in underestimating exposures for people who reside in areas with cleaner air but work in more polluted areas. Similarly, exposures were overestimated for people living in areas with poorer air quality and working in cleaner areas. These could lead to differential estimation biases across racial, ethnic and socioeconomic lines that typically correlate with where people live and work and lead to important exposure and health disparities. This study demonstrates that ignoring human movement and spatiotemporal variability of air pollution could lead to differential exposure misclassification potentially biasing health risk assessments. These improved dynamic approaches can help planners and policymakers identify disadvantaged populations for which exposures are typically misrepresented and might lead to targeted policy and planning implications.

Evaluation of daily time spent in transportation and traffic-influenced microenvironments by urban Canadians

Exposure to traffic and traffic-related air pollution is associated with a wide array of health effects. Time spent in a vehicle, in active transportation, along roadsides, and in close proximity to traffic can substantially contribute to daily exposure to air pollutants. For this study, we evaluated daily time spent in transportation and traffic-influenced microenvironments by urban Canadians using the Canadian Human Activity Pattern Survey (CHAPS) 2 results. Approximately 4-7% of daily time was spent in on- or near-road locations, mainly associated with being in a vehicle and smaller contributions from active transportation. Indoor microenvironments can be impacted by traffic emissions, especially when located near major roadways. Over 60% of the target population reported living within one block of a roadway with moderate to heavy traffic, which was variable with income level and city, and confirmed based on elevated NO₂ exposure estimated using land use regression. Furthermore, over 55% of the target population ≤ 18 years reported attending a school or daycare in close proximity to moderate to heavy traffic, and little variation was observed based on income or city. The results underline the importance of traffic emissions as a major source of exposure in Canadian urban centers, given the time spent in traffic-influenced microenvironments.

Assessment of Exposure to VOCs among Pregnant Women in the National Children's Study

Epidemiologic studies can measure exposure to volatile organic compounds (VOCs) using environmental samples, biomarkers, questionnaires, or observations. These different exposure assessment approaches each have advantages and disadvantages; thus, evaluating relationships is an important consideration. In the National Children's Vanguard Study from 2009 to 2010, participants completed questionnaires and data collectors observed VOC exposure sources and collected urine samples from 488 third trimester pregnant women at in-person study visits. From urine, we simultaneously quantified 28 VOC metabolites of exposure to acrolein, acrylamide, acrylonitrile, benzene, 1-bromopropane, 1,3-butadiene, carbon disulfide, crotonaldehyde, cyanide, N,N-dimethylformamide, ethylbenzene, ethylene oxide, propylene oxide, styrene, tetrachloroethylene, toluene, trichloroethylene, vinyl chloride, and xylene exposures using ultra high performance liquid chromatography coupled with an electrospray ionization tandem mass spectrometry (UPLC-ESI/MSMS) method. Urinary thiocyanate was measured using an ion chromatography coupled with an electrospray ionization tandem mass spectrometry method (IC-ESI/MSMS). We modeled the relationship between urinary VOC metabolite concentrations and sources of VOC exposure. Sources of exposure were assessed by participant report via questionnaire (use of air fresheners, aerosols, paint or varnish, organic solvents, and passive/active smoking) and by observations by a trained data collector (presence of scented products in homes). We found several significant (p < 0.01) relationships between the urinary metabolites of VOCs and sources of VOC exposure. Smoking was positively associated with metabolites of the tobacco constituents acrolein, acrylamide, acrylonitrile, 1,3-butadiene, crotonaldehyde, cyanide, ethylene oxide, N,N-dimethylformamide, propylene oxide, styrene, and xylene. Study location was negatively associated with the toluene metabolite N-acetyl-S-(benzyl)-L-cysteine (BMA), and paint use was positively associated with the xylene metabolites 2-methylhippuric acid (2MHA) and 3-Methylhippuric acid & 4-methylhippuric acid (3MHA + 4MHA). A near-significant (p = 0.06) relationship was observed between acrylamide metabolites and observation of incense.

Evaluating children's location using a personal GPS logging instrument: limitations and lessons learned

Global positioning system (GPS) technology is increasingly used to assess geographically varying exposure in population studies. However, there has been limited evaluation of accuracy and completeness of personal GPS data. The ability of a GPS data logger to assess location of children during usual activity was evaluated. Data collected for 4 days from 17 children wearing GPS loggers, recorded every 15 s, were evaluated for completeness by time of day during weekend and weekdays, and for accuracy during nighttime at home. Percentage of possible GPS-recorded points and of 5-min intervals with at least one recorded location were examined. Mean percentage of total possible 15-s interval locations recorded daily was less than 30%. Across participants, the GPS loggers recorded 1-47% of total possible location points on weekends and 1-55% on weekdays. More complete data were measured during travel to school (average 91%). The percentage of daily 5-min intervals with recorded data was as high as 53%. At least one location was recorded during 69% of 5-min intervals before school (0630-0800 h), 62% during school (0800-1400 h) and 56% after school (1400-1700 h). During night time (0000-0600 h), on average, location was recorded for less than 25% of 5-min intervals and accuracy was poor. The large proportion of missing data limits the usefulness of GPS logging instruments for population studies. They have potential utility for assessing on-road travel time and route. GPS technology has limitations, and lessons learned from this evaluation can be generalized to the use of GPS in other research settings.

Statistical properties of longitudinal time-activity data for use in human exposure modeling

Understanding the longitudinal properties of the time spent in different locations and activities is important in characterizing human exposure to pollutants. The results of a four-season longitudinal time-activity diary study in eight working adults are presented, with the goal of improving the parameterization of human activity algorithms in EPA's exposure modeling efforts. Despite the longitudinal, multi-season nature of the study, participant non-compliance with the protocol over time did not play a major role in data collection. The diversity (D)--a ranked intraclass correlation coefficient (ICC)-- and lag-one autocorrelation (A) statistics of study participants are presented for time spent in outdoor, motor vehicle, residential, and other-indoor locations. Day-type (workday versus non-workday, and weekday versus weekend), season, temperature, and gender differences in the time spent in selected locations and activities are described, and D & A statistics are presented. The overall D and ICC values ranged from approximately 0.08-0.26, while the mean population rank A values ranged from approximately 0.19-0.36. These statistics indicate that intra-individual variability exceeds explained inter-individual variability, and low day-to-day correlations among locations. Most exposure models do not address these behavioral characteristics, and thus underestimate population exposure distributions and subsequent health risks associated with environmental exposures.

Quantifying human exposure to air pollution--moving from static monitoring to spatio-temporally resolved personal exposure assessment

Quantifying human exposure to air pollutants is a challenging task. Ambient concentrations of air pollutants at potentially harmful levels are ubiquitous in urban areas and subject to high spatial and temporal variability. At the same time, every individual has unique activity-patterns. Exposure results from multifaceted relationships and interactions between environmental and human systems, adding complexity to the assessment process. Traditionally, approaches to quantify human exposure have relied on pollutant concentrations from fixed air quality network sites and static population distributions. New developments in sensor technology now enable us to monitor personal exposure to air pollutants directly while people are moving through their activity spaces and varying concentration fields. The literature review on which this paper is based on reflects recent developments in the assessment of human exposure to air pollution. This includes the discussion of methodologies and concepts, and the elaboration of approaches and study designs applied in the field. We identify shortcomings of current approaches and discuss future research needs. We close by proposing a novel conceptual model for the integrated assessment of human exposure to air pollutants taking into account latest technological capabilities and contextual information.

New approach for particulate exposure monitoring: determination of inhaled particulate mass by 24 h real-time personal exposure monitoring

The objectives of this study were to measure particulate pollution (PM(10), PM(2.5), and PM(1.0)) continuously (24 h/day for 7 day) using real-time exposure monitoring and to estimate total inhalation mass using breathing rate and time-activity. Breathing rates were calculated from measured heart rates. Participants were asked to record a time-activity diary every 15 min. Five microenvironments were defined based on the time-activity diary: home, workplace/school, other indoor, outdoor, and transportation. The average masses of inhaled PM(10) were 530, 316, and 280 μg/day for two office workers, a housewife, and three students, respectively; those of PM(2.5) were 316, 279, and 210 μg/day; and those of PM(1.0) were 251, 264, and 187 μg/day, respectively. We found that home and office/school microenvironments were the main contributors of PM(10), PM(2.5), and PM(1.0) inhaled mass during weekdays and weekends because dwelling time was a determinant factor for inhaled mass. Considering microenvironmental concentration, breathing rate, and dwelling time in each microenvironment, indoor home microenvironments were the largest source of particulate inhalation, followed in order by workplace, transportation, other indoor, and outdoor microenvironments. 34.6% and 69.6% of PM(10) inhalation mass were accumulated in home microenvironments during weekdays and weekends, respectively. The inhaled mass of particulate <1.0 μm (PM(1.0)) in size occupied largest, followed in order by particulate 10-2.5 μm (coarse particle) and 2.5-1.0 μm in size for all occupations.

Feasibility of using web surveys to collect time-activity data

Time-activity data are traditionally collected by telephone interviews or through paper diaries, which are time consuming and costly. As a potential alternative that may greatly save staff time, a web survey to collect time-activity data was developed and tested in this study. We collected 24-h recall web diaries from 151 parents of young children mostly under 55 years of age (who also answered for their children) and 55 older adults (≥ 55 years of age) both on a weekday and a weekend day every 3 months during an 18-month period. The performance and reliability of the web surveys collected were evaluated, including the survey-completion rate, and the percentage of surveys with unreasonable time being reported as spent sleeping and with missing reports of being in transit between locations. We also compared the web-survey data with time-activity information we collected from the same subjects in telephone interviews and found that these data sources were fairly consistent with each other. However, we observed slightly more compliance issues for the web than the telephone survey, but most of these issues could be addressed and minimized by refining some questions or the survey interface. Our study suggests that it is critical to reduce participants' burden and improve survey interface design for optimal compliance and data quality. In conclusion, web surveys are a promising method to consider for time-activity data collection.

Determinants of residential indoor and transportation activity times in Korea

Information on time spent in microenvironments has a critical role for personal exposure to environmental pollutants. Unlike several large-scale studies in Western countries, no comprehensive research on time-activity patterns for exposure assessment has been conducted in Korea. We investigated determinants of residential indoor and transportation times of individuals over 10-years old in the Korean population. The population-based study collected time-activity patterns of 31,634 Koreans for two consecutive days. The residential indoor and transportation times were collected for a weekday and a weekend day. The impact of sociodemographic factors on time-activity was assessed using multiple linear regression models. The residential indoor times were 14.23 h for the weekday and 16.13 h for the weekend and shorter than those in Western countries. The transportation times were 1.75 h for the weekday and 1.68 h for the weekend day. The most significant factors in residential indoor time were employment status, age, monthly income, and gender for the weekday and employment status and gender for the weekend day. The factors in transportation were gender, employment status, and monthly income for the weekday and gender, employment status, age, and marriage status for the weekend day. Determinants of the time-activity pattern need to be taken into account in exposure assessment, epidemiological analyses, and exposure simulations, as well as in the development of preventive strategies. As Korean population activity patterns are substantially different from those in Western countries such as USA, Germany, and UK, this information could be critical for exposure assessment in Korea and other Asian countries.

Time-location patterns of a population living in an air pollution hotspot

This study characterized the time-location pattern of 107 residents living in air pollution hotspots, the Waterfront South and Copewood/Davis Streets communities in Camden, NJ. Most residents in the two communities are minority and impoverished individuals. Results showed that employment status played the fundamental role in determining time-location patterns of this study population, and the variations of time-location pattern by season and by day-type were partially attributed to employment status. Compared to the National Human Activity Pattern Survey, the Camden cohort spent significantly more time outdoors (3.8 hours versus 1.8 hours) and less time indoors (19.4 hours versus 20.9 hours) than the general US population, indicating a higher risk of exposure to ambient air pollution for the Camden cohort. The findings of the study are important for understanding exposure routes and sources for the socioeconomically disadvantaged subgroup and ultimately help develop effective strategies to reduce community exposure to ambient air pollution in "hotspots".

Indoor time-microenvironment-activity patterns in seven regions of Europe

Personal exposure to environmental substances is largely determined by time-microenvironment-activity patterns while moving across locations or microenvironments. Therefore, time-microenvironment-activity data are particularly useful in modeling exposure. We investigated determinants of workday time-microenvironment-activity patterns of the adult urban population in seven European cities. The EXPOLIS study assessed workday time-microenvironment-activity patterns among a total of 1427 subjects (age 19-60 years) in Helsinki (Finland), Athens (Greece), Basel (Switzerland), Grenoble (France), Milan (Italy), Prague (Czech Republic), and Oxford (UK). Subjects completed time-microenvironment-activity diaries during two working days. We present time spent indoors--at home, at work, and elsewhere, and time exposed to tobacco smoke indoors for all cities. The contribution of sociodemographic factors has been assessed using regression models. More than 90% of the variance in indoor time-microenvironment-activity patterns originated from differences between and within subjects rather than between cities. The most common factors that were associated with indoor time-microenvironment-activity patterns, with similar contributions in all cities, were the specific work status, employment status, whether the participants were living alone, and whether the participants had children at home. Gender and season were associated with indoor time-microenvironment-activity patterns as well but the effects were rather heterogeneous across the seven cities. Exposure to second-hand tobacco smoke differed substantially across these cities. The heterogeneity of these factors across cities may reflect city-specific characteristics but selection biases in the sampled local populations may also explain part of the findings. Determinants of time-microenvironment-activity patterns need to be taken into account in exposure assessment, epidemiological analyses, exposure simulations, as well as in the development of preventive strategies that focus on time-microenvironment-activity patterns that ultimately determine exposures.

Comparison of global positioning system (GPS) tracking and parent-report diaries to characterize children's time-location patterns

Respondent error, low resolution, and study participant burden are known limitations of diary timelines used in exposure studies such as the National Human Exposure Assessment Survey (NHEXAS). Recent advances in global positioning system (GPS) technology have produced tracking devices sufficiently portable, functional and affordable to utilize in exposure assessment science. In this study, a differentially corrected GPS (dGPS) tracking device was compared to the NHEXAS diary timeline. The study also explored how GPS can be used to evaluate and improve such diary timelines by determining which location categories and which respondents are least likely to record "correct" time-location responses. A total of 31 children ages 3-5 years old wore a dGPS device for all waking hours on a weekend day while their parents completed the NHEXAS diary timeline to document the child's time-location pattern. Parents misclassified child time-location approximately 48% of the time using the NHEXAS timeline in comparison to dGPS. Overall concordance between methods was marginal (kappa=0.33-0.35). The dGPS device found that on average, children spent 76% of the 24-h study period in the home. The diary underestimated time the child spent in the home by 17%, while overestimating time spent inside other locations, outside at home, outside in other locations, and time spent in transit. Diary data for time spent outside at home and time in transit had the lowest response concordance with dGPS. The diaries of stay-at-home mothers and mothers working unskilled labor jobs had lower concordance with dGPS than did those of the other participants. The ability of dGPS tracking to collect continuous rather than categorical (ordinal) data was also demonstrated. It is concluded that automated GPS tracking measurements can improve the quality and collection efficiency of time-location data in exposure assessment studies, albeit for small cohorts.

Relationships of video assessments of touching and mouthing behaviors during outdoor play in urban residential yards to parental perceptions of child behaviors and blood lead levels

Childrens' touching and mouthing behaviors during outdoor play in urban residential yards were measured using video observations. Descriptions were made of childrens' outdoor residential play environments. Behaviors assessed were used to examine (1) validity of parental responses to questions on childrens' oral behaviors and outdoor play and (2) relationships of mouthing behaviors to blood lead levels (BLLs). Thirty-seven children aged 1-5 years were recruited for 2 h of video recording in their yard and blood lead measurement. Video assessments included hourly rates of hand touches to ground/walking-level surfaces (cement/stone/steel, porch floor/steps, grass, and bare soil) and oral behaviors. Parental questionnaires assessed their child's outdoor activities, behaviors, and home environment. The children were: mean 39 months; 51% male; 89% Hispanic; and 78% Medicaid or uninsured. Twenty-two children had a blood lead measured (mean 6 microg/dl). During taping, all children had access to cement, 92% to grass, 73% to bare soil, and 59% to an open porch. Children had frequent touching and mouthing behaviors observed (median touches/h: touches to surfaces 81; hand-to-mouth area (with and without food) 26; hand-in-mouth 7; and object-in-mouth 17). Blood lead was directly correlated with log-transformed rates of hand-in-mouth (Pearson's correlation, r=0.564, n=22, P=0.006) and object-in-mouth (Pearson's correlation, r=0.482, n=22, P=0.023) behaviors. Parental questionnaire responses did not accurately reflect childrens' observed oral behaviors, play habits, or play environment. These data confirm the direct relationship between hand-to-mouth activities and BLLs and fail to validate parental perceptions of their child's mouthing behaviors or outdoor play environment.

Risk factors for increased BTEX exposure in four Australian cities

Benzene, toluene, ethylbenzene and xylenes (BTEX) are common volatile organic compounds (VOCs) found in urban airsheds. Elevated levels of VOCs have been reported in many airsheds at many locations, particularly those associated with industrial activity, wood heater use and heavy traffic. Exposure to some VOCs has been associated with health risks. There have been limited investigations into community exposures to BTEX using personal monitoring to elucidate the concentrations to which members of the community may be exposed and the main contributors to that exposure. In this cross sectional study we investigated BTEX exposure of 204 non-smoking, non-occupationally exposed people from four Australian cities. Each participant wore a passive BTEX sampler over 24h on five consecutive days in both winter and summer and completed an exposure source questionnaire for each season and a diary for each day of monitoring. The geometric mean (GM) and range of daily BTEX concentrations recorded for the study population were benzene 0.80 (0.04-23.8 ppb); toluene 2.83 (0.03-2120 ppb); ethylbenzene 0.49 (0.03-119 ppb); and xylenes 2.36 (0.04-697 ppb). A generalised linear model was used to investigate significant risk factors for increased BTEX exposure. Activities and locations found to increase personal exposure included vehicle repair and machinery use, refuelling of motor vehicles, being in an enclosed car park and time spent undertaking arts and crafts. A highly significant difference was found between the mean exposures in each of the four cities, which may be explained by differences in fuel composition, differences in the mix and density of industry, density of motor vehicles and air pollution meteorology.

Using human activity data in exposure models: analysis of discriminating factors

This paper tests factors thought to be important in explaining the choices people make in where they spend time. Three aggregate locations are analyzed: outdoors, indoors, and in-vehicles for two different sample groups: a year-long (longitudinal) sample of one individual and a cross-sectional sample of 169 individuals from the US Environmental Protection Agency's Consolidated Human Activity Database (CHAD). The cross-sectional sample consists of persons similar to the longitudinal subject in terms of age, work status, education, and residential type. The sample groups are remarkably similar in the time spent per day in the tested locations, although there are differences in participation rates: the percentage of days frequenting a particular location. Time spent outdoors exhibits the most relative variability of any location tested, with in-vehicle time being the next. The factors found to be most important in explaining daily time usage in both sample groups are: season of the year, season/temperature combinations, precipitation levels, and day-type (work/nonwork is the most distinct, but weekday/weekend is also significant). Season, season/temperature, and day-type are also important for explaining time spent indoors. None of the variables tested are consistent in explaining in-vehicle time in either the cross-sectional or longitudinal samples. Given these findings, we recommend that exposure modelers subdivide their population activity data into at least season/temperature, precipitation, and day-type "cohorts" as these factors are important discriminating variables affecting where people spend their time.

Chapter one: exposure measurements

Determining human exposure to suspended particulate concentrations requires measurements that quantify different particle properties in microenvironments where people live, work, and play. Particle mass, size, and chemical composition are important exposure variables, and these are typically measured with time-integrated samples on filters that are later submitted to laboratory analyses. This requires substantial sample handling, quality assurance, and data reduction. Newer technologies are being developed that allow in-situ, time-resolved measurements for mass, carbon, sulfate, nitrate, particle size, and other variables. These are large measurement systems that are more suitable for fixed monitoring sites than for personal applications. Human exposure studies need to be designed to accomplish specific objectives rather than to serve too many purposes. Resources need to be divided among study design, field sampling, laboratory analysis, quality assurance, data management, and data analysis phases. Many exposure projects allocated too little to the non-measurement activities.

Chapter three: methodology of exposure modeling

In this chapter, the concept of exposure assessment and its evolution is introduced, and evaluated by critically appraising the pertinent literature as it applies to exposures to Particulate Matter (PM). Exposure measurement or estimation methodologies and models are reviewed. Three exposure/measurement methodologies are assessed. Estimation methods focus on source evaluation and attribution, sources include those outdoors and indoors as well as in occupational and in-transit environments. Fate and transport models and their inputs are addressed to estimate concentrations outdoors and indoors; source attribution techniques help focus on the contributing sources. Activity pattern techniques are also reviewed and their use in exposure models to estimate inhalation exposure to PM is presented. Deterministic, regression and other stochastic models of exposure to PM are reviewed and evaluated. Strengths, limitations, assumptions and affirmations of the use of exposure assessment as an integral component of risk assessment and risk management are discussed in the conclusions and discussions section of this work.

Methods for collecting time/activity pattern information related to exposure to combustion products

Focus groups, surveys and questionnaires, diaries and observations can be used to gather information about people's exposure to a wide range of combustion products. Information about locations and durations of exposure, and sources of exposure can be obtained with these instruments. The types of instruments used must be fine tuned to meet the design characteristics of the community in which the study will be conducted.

Use of global positioning system technology to track subject's location during environmental exposure sampling

Global positioning system (GPS) data recorders were worn by subjects in the Oklahoma Urban Air Toxics Study (OUATS) for automatic logging of their location as they went about their normal daily activities. The location information obtained by the GPS units had an uncertainty of about 10-20 m, which was sufficiently precise to track subjects' movements on trips outside the immediate vicinity of their homes. Due to instrument problems, primarily related to reduced battery life, the units operated for only about 30% of the total monitoring time attempted in 25 trials. The GPS data were compared to time-activity diaries kept by the subjects. In almost all cases, the GPS data confirmed all travel events reported in the subjects' diaries. Additionally, in five out of five trials in which the logging period covered most or all of the subjects' daytime activities, at least one travel event that was not recorded in the diary was detected by GPS. Notwithstanding the limitations of present technology, GPS was found to be a promising means for tracking of research subjects in community-based exposure assessment studies.