Variation In Personal Exposures Research Articles

Exploring the external exposome using wearable passive samplers - The China BAPE study.

Environmental exposures are one of the greatest threats to human health, yet we lack tools to answer simple questions about our exposures: what are our personal exposure profiles and how do they change overtime (external exposome), how toxic are these chemicals, and what are the sources of these exposures? To capture variation in personal exposures to airborne chemicals in the gas and particulate phases and identify exposures which pose the greatest health risk, wearable exposure monitors can be deployed. In this study, we deployed passive air sampler wristbands with 84 healthy participants (aged 60-69 years) as part of the Biomarkers for Air Pollutants Exposure (China BAPE) study. Participants wore the wristband samplers for 3 days each month for five consecutive months. Passive samplers were analyzed using a novel gas chromatography high resolution mass spectrometry data-processing workflow to overcome the bottleneck of processing large datasets and improve confidence in the resulting identified features. The toxicity of chemicals observed frequently in personal exposures were predicted to identify exposures of potential concern via inhalation route or other routes of airborne contaminant exposure. Three exposures were highlighted based on elevated toxicity: dichlorvos from insecticides (mosquito/malaria control), naphthalene partly from mothballs, and 183 polyaromatic hydrocarbons from multiple sources. Other exposures explored in this study are linked to diet and personal care products, cigarette smoke, sunscreen, and antimicrobial soaps. We highlight the potential for this workflow employing wearable passive samplers for prioritizing chemicals of concern at both the community and individual level, and characterizing sources of exposures for follow up interventions.

Assessing personal noise exposure and its relationship with mental health in Beijing based on individuals’ space-time behavior

BackgroundMost prior studies adopted a static residence-based approach in the assessment of noise exposure, which may lead to biased exposure estimates and misleading findings in noise–health relationships. Relatively little is known about personal noise exposure based on individuals’ space-time behavior and its effect on mental health. ObjectivesThis study aims to analyze and geo-visualize personal exposure to noise in various microenvironments based on individuals’ space-time trajectories at a very fine resolution and to further investigate the relationships between mental health and personal noise exposure at both the activity/travel episode level and the entire day level. MethodsIndividual-level real-time data were collected with portable noise sensors and GPS trackers from a sample of 117 residents aged 18–60 years old from December 2017 to February 2018 in Beijing, China. Descriptive statistics and geo-visualization methods were used to examine how personal noise exposure varied across different activity types, travel modes, and among residents living in the same residential neighborhood on workdays and weekends based on individuals’ space-time behaviors. Logistic regression models were applied to examine the relationships between personal noise exposure and self-reported mental health. ResultsWe observed substantial differences in personal noise exposure across different activity types. The equivalent sound levels (Leq, dB(A)) for sleeping were the lowest, while the average Leq for work-related activities was the highest in indoor environments. The noise exposure levels for activities in outdoor environments were higher than indoor noise levels but differed between workdays and weekends. Variations in noise exposure associated with different travel modes were also evident, with the average Leq for public transport being much higher than that of other travel modes. A-weighted equivalent sound pressure level measured over 24 h for each individual (Leq,24h, dB(A)) varied significantly for residents living in the same residential neighborhood, ranging from 36 to 97 dB(A), with the majority of respondents being exposed to noise levels above 55 dB(A) on both workdays and weekends. Regarding the noise–health relationships, the modeling results showed that individual-level objective noise exposure based on space-time behaviors measured over a 24-h period (Leq,24h) was strongly associated with residents’ self-reported mental health. Higher exposure to noise was significantly associated with worse mental health. However, personal noise exposure at the activity/travel episode level (Leq) was not significantly associated with mental health on weekdays, but this link turned out to be significant in the weekend model. ConclusionsThere were large variations in personal noise exposure associated with different activity types and travel modes, and the individual-level noise exposure varied significantly across time of day and between residents living in the same residential neighborhood. Variations in personal exposure strongly depend on different space-time behaviors and individual-specific microenvironments experienced in daily life, and they were significantly correlated with mental health.

A modelling exercise to examine variations of NOx concentrations on adjacent footpaths in a street canyon: The importance of accounting for wind conditions and fleet composition

Personal measurement studies and modelling investigations are used to examine pollutant exposure for pedestrians in the urban environment: each presenting various strengths and weaknesses in relation to labour and equipment costs, a sufficient sampling period and the accuracy of results. This modelling exercise considers the potential benefits of modelling results over personal measurement studies and aims to demonstrate how variations in fleet composition affects exposure results (presented as mean concentrations along the centre of both footpaths) in different traffic scenarios. A model of Pearse Street in Dublin, Ireland was developed by combining a computational fluid dynamic (CFD) model and a semi-empirical equation to simulate pollutant dispersion in the street. Using local NOx concentrations, traffic and meteorological data from a two-week period in 2011, the model were validated and a good fit was presented. To explore the long-term variations in personal exposure due to variations in fleet composition, synthesised traffic data was used to compare short-term personal exposure data (over a two-week period) with the results for an extended one-year period. Personal exposure during the two-week period underestimated the one-year results by between 8% and 65% on adjacent footpaths. The findings demonstrate the potential for relative differences in pedestrian exposure to exist between the north and south footpaths due to changing wind conditions in both peak and off-peak traffic scenarios. This modelling approach may help overcome potential under- or over-estimations of concentrations in personal measurement studies on the footpaths. Further research aims to measure pollutant concentrations on adjacent footpaths in different traffic and wind conditions and to develop a simpler modelling system to identify pollutant hotspots on our city footpaths so that urban planners can implement improvement strategies to improve urban air quality.

Influence of Differing Microenvironments on Personal Carbon Monoxide Exposure in Auckland, New Zealand

Epidemiological studies typically estimate pollutant exposures using data from outdoor fixed monitoring stations (FMS). However, due to individual mobility through space, time spent in indoor environments and the heterogeneity of the urban atmosphere, data from FMS provides a poor representation of the actual personal exposure to air pollutants. The aim of this study is to investigate the relative importance of time spent in common microenvironments (such as commuter, home, work and recreational) to determine personal exposure to air pollution. The study also investigates the extent to which fixed monitoring stations (FMS) are representative of personal exposures. For this purpose, 17 participants monitored their personal exposure to carbon monoxide (CO) for a full working week and completed a time activity diary identifying the particular microenvironments in which they spent their time. Overall, the participants exposure to CO were lower than those observed in other northern hemisphere cities reported upon in the literature. FMS located in central Auckland were found to provide reasonable estimates of mean daily personal exposure but were poorly correlated with diurnal variations in personal exposure. The results found that, while the highest mean exposures were recorded in the commuter microenvironment, the home microenvironment accounted for 55% of the total CO dose during the week. Increased levels of personal CO exposure were observed in indoor areas where gas heating, gas stoves and tobacco smoke were present. Participants recorded highly variable exposure to CO in recreational microenvironments, in part explained by the wide range of recreational activities.

Spatial Variation in Personal Exposure of Parking Attendants to Traffic Emissions in an Urban Conurbation

This study presents temporal and spatial variations in personal exposure of parking attendants in a busy conurbation in the northern part of the UK. Two traffic related pollutants - carbon monoxide (CO) and ultrafine particulates (UFP), mainly associated with urban drives, have been considered for two distinct locations- one, in the city centre and the other in a suburban area of Leeds, a prominent city in West Yorkshire. The monitoring of pollutants was conducted while parking attendants carried out their duty along the streets during different times of the year to capture the seasonal fluctuations. Our results show a wide variation in exposure levels for both CO and UFP, marked both by seasonal and daily characteristics. There seems to be considerable variations in exposure levels depending on the location of the parking attendants with respect to traffic activity. Specifically, the level of exposure closer to market areas within the city centre, despite located in open spaces but closer to congested streets were found to be much higher owing to frequent stopping, stopping and idling of cars in search of parking spaces. This demonstrates the merit in setting up transport interchanges and park-and-ride schemes away from busy streets to ameliorate the exposure levels.

Measuring combined exposure to environmental pressures in urban areas: An air quality and noise pollution assessment approach

This study presents a methodological scheme developed to provide a combined air and noise pollution exposure assessment based on measurements from personal portable monitors. Provided that air and noise pollution are considered in a co-exposure approach, they represent a significant environmental hazard to public health. The methodology is demonstrated for the city of Thessaloniki, Greece. The results of an extensive field campaign are presented and the variations in personal exposure between modes of transport, routes, streets and transport microenvironments are evaluated. Air pollution and noise measurements were performed simultaneously along several commuting routes, during the morning and evening rush hours. Combined exposure to environmental pollutants is highlighted based on the Combined Exposure Factor (CEF) and Combined Dose and Exposure Factor (CDEF). The CDEF takes into account the potential relative uptake of each pollutant by considering the physical activities of each citizen. Rather than viewing environmental pollutants separately for planning and environmental sustainability considerations, the possibility of an easy-to-comprehend co-exposure approach based on these two indices is demonstrated. Furthermore, they provide for the first time a combined exposure assessment to these environmental pollutants for Thessaloniki and in this sense they could be of importance for local public authorities and decision makers. A considerable environmental burden for the citizens of Thessaloniki, especially for VOCs and noise pollution levels is observed. The material herein points out the importance of measuring public health stressors and the necessity of considering urban environmental pollution in a holistic way.

Predicting Personal Nitrogen Dioxide Exposure in an Elderly Population: Integrating Residential Indoor and Outdoor Measurements, Fixed-Site Ambient Pollution Concentrations, Modeled Pollutant Levels, and Time–Activity Patterns

Predicting chronic exposure to air pollution at the intra-urban scale has been recognized as a priority area of research for environmental epidemiology. Exposure assessment models attempt to predict and proxy for individuals' personal exposure to ambient air pollution, and there are no studies to date that explicitly attempt to compare and cross-validate personal exposure concentrations with pollutants modeled at the intra-urban level using methods such as interpolated surfaces and land-use regression (LUR) models. This study aimed to identify how well personal exposure to NO2 (nitrogen dioxide) can be predicted from ambient exposure measurements and intra-urban exposure estimates using LUR and what other factors contribute to predicting variations in personal exposure beyond measured pollutant levels within home. Personal, indoor and outdoor NO2 were measured in a population of older adults (>65 yr old) living in Hamilton, Canada. Our results show that personal NO2 was most strongly associated with contemporaneously collected indoor and outdoor concentrations of NO2. Predicted NO2 exposures from intra-urban LUR models were not associated with personal NO2, whereas interpolated surfaces of particulates and ozone were modestly associated. Combinations of variables that best predicted personal NO2 variability were derived from time–activity diaries, interpolated surfaces of ambient particulate pollutants, and a citywide temporally matched average of NO2. The nonsignificant associations between personal NO2 and the modeled ambient NO2 concentrations suggest that observed associations between NO2 generated by LUR models and health effects are probably not produced by NO2, but by other pollutants that follow a similar spatial pattern.

Profiling transient daytime peaks in urban air pollutants: city centre traffic hotspot versus urban background concentrations

Averaged annual and daily limit values imposed for the protection of human health from urban air pollutants fail to address the potential influence of variations in personal exposure during the day, even though asthma attacks, myocardial ischaemia, and other effects have been linked to highly transient rises in atmospheric contamination. There is, therefore, a general need for the wider availability of information on hourly variations in urban pollutant levels, as well as on spatial differences between urban background and localised contamination hotspots. We compare data from urban background and traffic hotspot monitoring stations over a 12-month period to demonstrate the hourly flux of air pollutants, notably NO(x), SO(2) and PM(10), and illustrate the effects of controlling influences, such as traffic movements and exotic dust intrusions. Our methodology focuses on daytime transient peaks, which occur when much of the urban population is awake and active, and when striking increases in pollutant concentrations are especially common at traffic hotspots. Greater emphasis on the daily reality of transient peaks in urban atmospheric pollutants offers a higher degree of spatial and temporal data resolution for use in epidemiological studies.

Exposure and measurement contributions to estimates of acute air pollution effects

Air pollution health effect studies are intended to estimate the effect of a pollutant on a health outcome. The definition of this effect depends upon the study design, disease model parameterization, and the type of analysis. Further limitations are imposed by the nature of exposure and our ability to measure it. We define a plausible exposure model for air pollutants that are relatively nonreactive and discuss how exposure varies. We discuss plausible disease models and show how their parameterizations are affected by different exposure partitions and by different study designs. We then discuss a measurement model conditional on ambient concentrations and incorporate this into the disease model. We use simulation studies to show the impact of a range of exposure model assumptions on estimation of the health effect in the ecologic time series design. This design only uses information from the time-varying ambient source exposure. When ambient and nonambient sources are independent, exposure variation due to nonambient source exposures behaves like Berkson measurement error and does not bias the effect estimates. Variation in the population attenuation of ambient concentrations over time does bias the estimates with the bias being either positive or negative depending upon the association of this parameter with ambient pollution. It is not realistic to substitute measured average personal exposures into time series studies because so much of the variation in personal exposures comes from nonambient sources that do not contribute information in the time series design. We conclude that general statements about the implications of measurement error need to be conditioned on the health effect study design and the health effect parameter to be estimated.

Sociodemographic descriptors of personal exposure to fine particles (PM2.5) in EXPOLIS Helsinki.

Demographic and socioeconomic differences between population sub-groups were analyzed, as a component of the EXPOLIS (Air Pollution Exposure Distributions Within Adult Urban Populations in Europe) Helsinki study, to explain variation in personal exposures to fine particles (PM2.5). Two-hundred one individuals were randomly selected among 25--55-year-old inhabitants of Helsinki Metropolitan area. Personal exposure samples and residential indoor, residential outdoor and workplace indoor microenvironment measurements of PM2.5 were collected between October 1996 and December 1997. Variation in PM2.5 personal exposures, between sociodemographic sub-groups, was best described by differences in occupational status, education and age. Lower occupational status, less educated and young participants had greater exposures than upper occupational status, more educated and older participants. Different workplace concentrations explained most of the socioeconomic differences, and personal day and night exposures and concentrations in home (but not workplace or outdoor concentrations) caused the PM2.5 exposure differences between age groups. Men had higher exposures and much larger exposure differences between the sociodemographic groups than women. No gender, socioeconomic or age differences were observed in home outdoor concentrations between groups. Exposure to tobacco smoke did not seem to create new differences between the sociodemographic groups; instead, it amplified the existing differences.

Personal exposures of children to nitrogen dioxide relative to concentrations in outdoor air

OBJECTIVESTo investigate the relation between fluctuations in personal exposure to nitrogen dioxide (NO2) in school children and changes in outdoor NO2 concentrations.METHODS114 Asthmatic school children aged 7–12 years were recruited...

Personal Exposure to Nitrogen Dioxide in the Los Angeles Basin

A large scale study of human exposure to nitrogen dioxide (NO2) was conducted in the Los Angeles Basin, the only metropolitan area in the United States that exceeds the NO2 NAAQS. Data are available for a population representative sample of 482 households and 682 individuals. Personal exposures, as well as indoor and outdoor home levels, were monitored using passive time-integrating filter badges. Monitoring extended over a one-year period (May 1987 to May 1988), with each individual providing two consecutive days of data. Information was also collected on activity patterns, household and personal characteristics, and spatial and temporal variables. This paper describes the study design, summarizes the sample characteristics and representativeness, and presents the distribution of personal, indoor, and outdoor NO2 concentrations recorded by the monitors. Over the entire sample, median personal and outdoor levels were 35 ppb; median indoor levels were 24 ppb. Personal exposures for those in homes with gas ranges with pilot lights average 10 ppb greater than those with electric ranges, and 4 ppb greater than those with gas ranges without pilot lights. Forty percent of the variation in indoor concentrations is explained by outdoor levels; 59 percent of the variation in personal exposures is explained by indoor levels; and 48 percent of the variation in personal exposures is explained by outdoor levels.