Implementation and validation of a modeling framework to assess personal exposure to black carbon

Does residential address-based exposure assessment for outdoor air pollution lead to bias in epidemiological studies?

BACKGROUND

Epidemiological studies of long-term exposure to outdoor air pollution have consistently documented associations with morbidity and mortality. Air pollution exposure in these epidemiological studies is generally assessed at the residential address, because individual time-activity patterns are seldom known in large epidemiological studies. Ignoring time-activity patterns may result in bias in epidemiological studies. The aims of this paper are to assess the agreement between exposure assessed at the residential address and exposures estimated with time-activity integrated and the potential bias in epidemiological studies when exposure is estimated at the residential address.

MAIN BODY

We reviewed exposure studies that have compared residential and time-activity integrated exposures, with a focus on the correlation. We further discuss epidemiological studies that have compared health effect estimates between the residential and time-activity integrated exposure and studies that have indirectly estimated the potential bias in health effect estimates in epidemiological studies related to ignoring time-activity patterns. A large number of studies compared residential and time-activity integrated exposure, especially in Europe and North America, mostly focusing on differences in level. Eleven of these studies reported correlations, showing that the correlation between residential address-based and time-activity integrated long-term air pollution exposure was generally high to very high (R > 0.8). For individual subjects large differences were found between residential and time-activity integrated exposures. Consistent with the high correlation, five of six identified epidemiological studies found nearly identical health effects using residential and time-activity integrated exposure. Six additional studies in Europe and North America showed only small to moderate potential bias (9 to 30% potential underestimation) in estimated exposure response functions using residence-based exposures. Differences of average exposure level were generally small and in both directions. Exposure contrasts were smaller for time-activity integrated exposures in nearly all studies. The difference in exposure was not equally distributed across the population including between different socio-economic groups.

CONCLUSIONS

Overall, the bias in epidemiological studies related to assessing long-term exposure at the residential address only is likely small in populations comparable to those evaluated in the comparison studies. Further improvements in exposure assessment especially for large populations remain useful.

Validity of Mobility-Based Exposure Assessment of Air Pollution: A Comparative Analysis with Home-Based Exposure Assessment

Air pollution exposure is typically assessed at the front door where people live in large-scale epidemiological studies, overlooking individuals' daily mobility out-of-home. However, there is limited evidence that incorporating mobility data into personal air pollution assessment improves exposure assessment compared to home-based assessments. This study aimed to compare the agreement between mobility-based and home-based assessments with personal exposure measurements. We measured repeatedly particulate matter (PM2.5) and black carbon (BC) using a sample of 41 older adults in the Netherlands. In total, 104 valid 24 h average personal measurements were collected. Home-based exposures were estimated by combining participants' home locations and temporal-adjusted air pollution maps. Mobility-based estimates of air pollution were computed based on smartphone-based tracking data, temporal-adjusted air pollution maps, indoor-outdoor penetration, and travel mode adjustment. Intraclass correlation coefficients (ICC) revealed that mobility-based estimates significantly improved agreement with personal measurements compared to home-based assessments. For PM2.5, agreement increased by 64% (ICC: 0.39-0.64), and for BC, it increased by 21% (ICC: 0.43-0.52). Our findings suggest that adjusting for indoor-outdoor pollutant ratios in mobility-based assessments can provide more valid estimates of air pollution than the commonly used home-based assessments, with no added value observed from travel mode adjustments.

OLYMPUS-POPGEN: A synthetic population generation model to represent urban populations for assessing exposure to air quality

SCIENTIFIC QUESTION

With the new individual- and activity-based approaches to simulating exposure to air pollutants, exposure models must now provide synthetic populations that realistically reflect the demographic profiles of individuals in an urban territory. Demographic profiles condition the behavior of individuals in urban space (activities, mobility) and determine the resulting risks of exposure and environmental inequalities. In this context, there is a strong need to determine the relevance of the population modeling methods to reproduce the combinations of socio-demographic parameters in a population from the existing databases. The difficulty of accessing complete, high-resolution databases indeed proves to be very limiting for the ambitions of the different approaches.

OBJECTIVE

This work proposes to evaluate the potential of a statistical approach for the numerical modeling of synthetic populations, at the scale of dwellings and including the representation of coherent socio-demographic profiles. The approach is based on and validated against the existing open databases. The ambition is to be able to build upon such synthetic populations to produce a comprehensive assessment of the risk of environmental exposure that can be cross-referenced with lifestyles, indicators of social, professional or demographic category, and even health vulnerability data.

METHOD

The approach implemented here is based on the use of conditional probabilities to model the socio-demographic properties of individuals, via the deployment of a Monte Carlo Markov Chain (MCMC) simulation. Households are assigned to housing according to income and house price classes. The resulting population generation model was tested in the Paris region (Ile de France) for the year 2010, and applied to a population of almost 12 million individuals. The approach is based on the use of census and survey databases.

RESULTS

Validation, carried out by comparison with regional census data, shows that the model accurately reproduces the demographic attributes of individuals (age, gender, professional category, income) as well as their combination, at both regional and sub-municipal levels. Notably, population distribution at the scale of the model buildings remains consistent with observed data patterns.

CONCLUSIONS AND RELEVANCE

The outcomes of this work demonstrate the ability of our approach to create, from public data, a coherent synthetic population with broad socio-demographic profiles. They give confidence for the use of this approach in an activity-based air quality exposure study, and thus for exploring the interrelations between social determinants and environmental risks. The non-specific nature of this work allows us to consider its extension to broader demographic profiles, including health indicators, and to different study regions.

The health impact of freight transport-related air pollution on vulnerable population groups

Every year, over 364,200 people in Europe die prematurely due to the effects of air pollution, in which the transportation sector plays an important role. In Brussels, freight transport generates €61,604 of air pollution health costs daily. Research has shown that dynamic spatiotemporal modeling of both emission sources and exposed people (using mobile phone data) renders more accurate impact results when analyzed in microenvironments. However, mobile data underrepresent population segments that are more sensitive to the effects of air pollution, such as toddlers, children and elderly individuals. This paper examined the link between vulnerable people aged 0-3, 3-18 and >65 years and freight transport-related air pollution concentrations in the Brussels-Capital Region (BCR). To this end, dynamic tailpipe emissions and their spatiotemporal dispersion were calculated using output from the Transport Agent-Based Model (TRABAM) on a daily basis. Population densities were calculated as a function of the residences' occupancy rate and school/class size and opening hours. The effects of exposure were then evaluated using age- and sex-differentiated exposure-response functions and monetized using local hospital cost factors. Data were compiled for 2021. A strong overlap between people's presence at the institutions' locations was noticed with a peak in (freight) transportation movements in the city. The results showed that €37,000 [€34,517.47-€40,047.13] of freight transport-related air pollution health costs were incurred daily by vulnerable population segments. While these vulnerable groups made up 25.34% of the total BCR population, they incurred 60% [56.03%-65.01%] of the engendered transportation air pollution costs. The results were then geographically analyzed to identify 465 traffic-related air pollution hotspots across the territory, which accounted for €36,000 [€33,677.85-€39,101.31] of total costs. The latter can be used in future studies to assess sector-specific freight transportation policies, which should take into consideration spatiotemporal population densities on the local level.

Long-term commuting times and air quality relationship to COVID-19 in São Paulo

The Coronavirus Disease 2019 (COVID-19) epidemic is an unprecedented global health crisis and the effects may be related to environmental and socio-economic factors. In São Paulo, Brazil, the first death occurred in March 2020 and since then the numbers have grown to 175 new deaths per day in April 2021, positioning the city as the epicenter of the number of cases and deaths in Brazil. São Paulo is one of the largest cities in the world with more than 12 million inhabitants, a fleet of about 8 million vehicles and frequent pollutant concentrations above recommended values. Social inequalities are evident in the municipality, similarly to other cities in the world. This paper focuses on transportation activities related to air pollution and associated with cardiovascular and respiratory diseases especially on people who developed comorbidities during their whole life. This study relates travel trip data to air quality analysis and expanded to COVID-19 disease. This work studied the relationship of deaths in São Paulo due to COVID-19 with demographic density, with family income, with the use of public transport and with atmospheric pollution for the period between March 17th, 2020 and April 29th, 2021. The main results showed that generally passenger kilometers traveled, commuting times and air quality related diseases increase with residential distance from the city center, and thus, with decreasing residential density. PM2.5 concentrations are positively correlated with COVID-19 deaths, regions with high urban densities have higher numbers of deaths and long-distance frequent trips can contribute to spread of the disease.

Environmental exposure during travel: A research review and suggestions forward

Daily travel through the urban fabric exposes urban dwellers to a range of environmental conditions that may have an impact on their health and wellbeing. Knowledge about exposures during travel, their associations with travel behavior, and their social and health outcomes are still limited. In our review, we aim to explain how the current environmental exposure research addresses the interactions between human and environmental systems during travel through their spatial, temporal and contextual dimensions. Based on the 104 selected studies, we identify significant recent advances in addressing the spatiotemporal dynamics of exposure during travel. However, the conceptual and methodological framework for understanding the role of multiple environmental exposures in travel environments is still in an early phase, and the health and wellbeing impacts at individual or population level are not well known. Further research with greater geographical balance is needed to fill the gaps in the empirical evidence, and linking environmental exposures during travel with the causal health and wellbeing outcomes. These advancements can enable evidence-based urban and transport planning to take the next step in advancing urban livability.

Prediction of human active mobility in rural areas: development and validity tests of three different approaches

BACKGROUND/AIM

Active mobility may play a relevant role in the assessment of environmental exposures (e.g. traffic-related air pollution, livestock emissions), but data about actual mobility patterns are work intensive to collect, especially in large study populations, therefore estimation methods for active mobility may be relevant for exposure assessment in different types of studies. We previously collected mobility patterns in a group of 941 participants in a rural setting in the Netherlands, using week-long GPS tracking. We had information regarding personal characteristics, self-reported data regarding weekly mobility patterns and spatial characteristics. The goal of this study was to develop versatile estimates of active mobility, test their accuracy using GPS measurements and explore the implications for exposure assessment studies.

METHODS

We estimated hours/week spent on active mobility based on personal characteristics (e.g. age, sex, pre-existing conditions), self-reported data (e.g. hours spent commuting per bike) or spatial predictors such as home and work address. Estimated hours/week spent on active mobility were compared with GPS measured hours/week, using linear regression and kappa statistics.

RESULTS

Estimated and measured hours/week spent on active mobility had low correspondence, even the best predicting estimation method based on self-reported data, resulted in a R² of 0.09 and Cohen's kappa of 0.07. A visual check indicated that, although predicted routes to work appeared to match GPS measured tracks, only a small proportion of active mobility was captured in this way, thus resulting in a low validity of overall predicted active mobility.

CONCLUSIONS

We were unable to develop a method that could accurately estimate active mobility, the best performing method was based on detailed self-reported information but still resulted in low correspondence. For future studies aiming to evaluate the contribution of home-work traffic to exposure, applying spatial predictors may be appropriate. Measurements still represent the best possible tool to evaluate mobility patterns.

Evaluation of wearable sensors for physiologic monitoring of individually experienced temperatures in outdoor workers in southeastern U.S

Climate-related increases in global mean temperature and the intensification of heat waves present a significant threat to outdoor workers. Limited research has been completed to assess the potential differences in heat exposures that exist between individuals within similar microenvironments. Yet, there is a paucity of individual data characterizing patterns of individually experienced temperatures in workers and the associated physiologic heat strain response. The objective of this study was to apply a wearable sensor-based approach to examine the occupational, environmental, and behavioral factors that contribute to individual-level variations in heat strain in grounds maintenance workers. Outdoor workers from three diverse climatic locations in the southeastern United States - high temperature, high temperature + high humidity, and moderate temperature environments - participated in personal heat exposure monitoring during a 5-day work period in the summer. We performed Cox proportional hazards modeling to estimate associations between multiple heat strain events per worker and changes in individually experienced temperatures. Heat strain risk was higher among workers with a place to cool-off, higher education, and who worked in hotter temperatures. A mismatch was observed between workers' perceptions of heat strain and actual heat strain prevalence across exposure groups. We also used a quasi-Poisson regression with distributed lag non-linear function to estimate the non-linear and lag effects of individually experienced temperatures on risk of heat strain. The association between increasing temperature and heat strain was nonlinear and exhibited an U-shaped relationship. Heat strain was less common during issued heat warnings demonstrating behavioral adaptive actions taken by workers. This study is one of the first temperature monitoring studies to quantify the individual-level exposure-response function in this vulnerable population and highlights the elevated risk of heat strain both immediately and several days after worker exposure to high temperatures.

Variations in individuals' exposure to black carbon particles during their daily activities: a screening study in Brazil

Black carbon (BC) is a fraction of airborne PM_2.5 emitted by combustion, causing deleterious effects on human health. Due to its abundance in cities, assessing personal exposure to BC is of utmost importance. Personal exposure and dose of six couples with different working routines were determined for 48 h based on 1-min mobile BC measurements and on ambient concentrations monitored simultaneously at home (outdoor) and at a suburban site. Although couples spent on average ~ 10 h together at home, the routine of each individual in other microenvironments led to 3-55% discrepancies in exposure between partners. The location of the residences and background concentrations accounted for the differences in inter-couple exposure. The overall average exposure and dose by gender were not statistically different. The personal exposure and dose calculated with datasets from fixed sites were lower than the calculations using data from mobile measurements, with the largest divergences (between four and nine times) in the transport category. Even though the individuals spent only 7% of the time commuting, this activity contributed to between 17 and 20% of the integrated exposure and inhaled dose, respectively. On average, exposure was highest on bus trips, while pedestrians and bus passengers had lower doses. Open windows elevated the in-car exposure and dose four times compared to settings with closed windows.

Spatial and Temporal Dynamics in Air Pollution Exposure Assessment

Analyzing individual exposure in urban areas offers several challenges where both the individual's activities and air pollution levels demonstrate a large degree of spatial and temporal dynamics. This review article discusses the concepts, key elements, current developments in assessing personal exposure to urban air pollution (seventy-two studies reviewed) and respective advantages and disadvantages. A new conceptual structure to organize personal exposure assessment methods is proposed according to two classification criteria: (i) spatial-temporal variations of individuals' activities (point-fixed or trajectory based) and (ii) characterization of air quality (variable or uniform). This review suggests that the spatial and temporal variability of urban air pollution levels in combination with indoor exposures and individual's time-activity patterns are key elements of personal exposure assessment. In the literature review, the majority of revised studies (44 studies) indicate that the trajectory based with variable air quality approach provides a promising framework for tackling the important question of inter- and intra-variability of individual exposure. However, future quantitative comparison between the different approaches should be performed, and the selection of the most appropriate approach for exposure quantification should take into account the purpose of the health study. This review provides a structured basis for the intercomparing of different methodologies and to make their advantages and limitations more transparent in addressing specific research objectives.

Comparison of air pollution exposures in active vs. passive travel modes in European cities: A quantitative review

BACKGROUND

Transport microenvironments tend to have higher air pollutant concentrations than other settings most people encounter in their daily lives. The choice of travel modes may affect significantly individuals' exposures; however such considerations are typically not accounted for in exposure assessment used in environmental health studies. In particular, with increasing interest in the promotion of active travel, health impact studies that attempt to estimate potential adverse consequences of potential increased pollutant inhalation during walking or cycling have emerged. Such studies require a quantification of relative exposures in travel modes.

METHODS

The literature on air pollution exposures in travel microenvironments in Europe was reviewed. Studies which measured various travel modes including at least walking or cycling in a simultaneous or quasi-simultaneous design were selected. Data from these studies were harmonized to allow for a quantitative synthesis of the estimates. Ranges of ratios and 95% confidence interval (CI) of air pollution exposure between modes and between background and transportation modes were estimated.

RESULTS

Ten studies measuring fine particulate matter (PM_2.5), black carbon (BC), ultrafine particles (UFP), and/or carbon monoxide (CO) in the walk, bicycle, car and/or bus modes were included in the analysis. Only three reported on CO and BC and results should be interpreted with caution. Pedestrians were shown to be the most consistently least exposed of all across studies, with the bus, bicycle and car modes on average 1.3 to 1.5 times higher for PM_2.5; 1.1 to 1.7 times higher for UFP; and 1.3 to 2.9 times higher for CO; however the 95% CI included 1 for the UFP walk to bus ratio. Only for BC were pedestrians more exposed than bus users on average (bus to walk ratio 0.8), but remained less exposed than those on bicycles or in cars. Car users tended to be the most exposed (from 2.9 times higher than pedestrians for BC down to similar exposures to cyclists for UFP on average). Bus exposures tended to be similar to that of cyclists (95% CI including 1 for PM_2.5, CO and BC), except for UFP where they were lower (ratio 0.7).

CONCLUSION

A quantitative method that synthesizes the literature on air pollution exposure in travel microenvironments for use in health impact assessments or potentially for epidemiology was conducted. Results relevant for the European context are presented, showing generally greatest exposures in car riders and lowest exposure in pedestrians.

Assessing the Suitability of Multiple Dispersion and Land Use Regression Models for Urban Traffic-Related Ultrafine Particles

Comparative evaluations are needed to assess the suitability of near-road air pollution models for traffic-related ultrafine particle number concentration (PNC). Our goal was to evaluate the ability of dispersion (CALINE4, AERMOD, R-LINE, and QUIC) and regression models to predict PNC in a residential neighborhood (Somerville) and an urban center (Chinatown) near highways in and near Boston, Massachusetts. PNC was measured in each area, and models were compared to each other and measurements for hot (>18 °C) and cold (<10 °C) hours with wind directions parallel to and perpendicular downwind from highways. In Somerville, correlation and error statistics were typically acceptable, and all models predicted concentration gradients extending ∼100 m from the highway. In contrast, in Chinatown, PNC trends differed among models, and predictions were poorly correlated with measurements likely due to effects of street canyons and nonhighway particle sources. Our results demonstrate the importance of selecting PNC models that align with study area characteristics (e.g., dominant sources and building geometry). We applied widely available models to typical urban study areas; therefore, our results should be generalizable to models of hourly averaged PNC in similar urban areas.

Dynamic assessment of exposure to air pollution using mobile phone data

Background

Exposure to air pollution can have major health impacts, such as respiratory and cardiovascular diseases. Traditionally, only the air pollution concentration at the home location is taken into account in health impact assessments and epidemiological studies. Neglecting individual travel patterns can lead to a bias in air pollution exposure assessments.

Methods

In this work, we present a novel approach to calculate the daily exposure to air pollution using mobile phone data of approximately 5 million mobile phone users living in Belgium. At present, this data is collected and stored by telecom operators mainly for management of the mobile network. Yet it represents a major source of information in the study of human mobility. We calculate the exposure to NO₂ using two approaches: assuming people stay at home the entire day (traditional static approach), and incorporating individual travel patterns using their location inferred from their use of the mobile phone network (dynamic approach).

Results

The mean exposure to NO₂ increases with 1.27 μg/m³ (4.3 %) during the week and with 0.12 μg/m³ (0.4 %) during the weekend when incorporating individual travel patterns. During the week, mostly people living in municipalities surrounding larger cities experience the highest increase in NO₂ exposure when incorporating their travel patterns, probably because most of them work in these larger cities with higher NO₂ concentrations.

Conclusions

It is relevant for health impact assessments and epidemiological studies to incorporate individual travel patterns in estimating air pollution exposure. Mobile phone data is a promising data source to determine individual travel patterns, because of the advantages (e.g. low costs, large sample size, passive data collection) compared to travel surveys, GPS, and smartphone data (i.e. data captured by applications on smartphones).

Urban and transport planning, environmental exposures and health-new concepts, methods and tools to improve health in cities

BACKGROUND

The majority of people live in cities and urbanization is continuing worldwide. Cities have long been known to be society's predominant engine of innovation and wealth creation, yet they are also a main source of pollution and disease.

METHODS

We conducted a review around the topic urban and transport planning, environmental exposures and health and describe the findings.

RESULTS

Within cities there is considerable variation in the levels of environmental exposures such as air pollution, noise, temperature and green space. Emerging evidence suggests that urban and transport planning indicators such as road network, distance to major roads, and traffic density, household density, industry and natural and green space explain a large proportion of the variability. Personal behavior including mobility adds further variability to personal exposures, determines variability in green space and UV exposure, and can provide increased levels of physical activity. Air pollution, noise and temperature have been associated with adverse health effects including increased morbidity and premature mortality, UV and green space with both positive and negative health effects and physical activity with many health benefits. In many cities there is still scope for further improvement in environmental quality through targeted policies. Making cities 'green and healthy' goes far beyond simply reducing CO2 emissions. Environmental factors are highly modifiable, and environmental interventions at the community level, such as urban and transport planning, have been shown to be promising and more cost effective than interventions at the individual level. However, the urban environment is a complex interlinked system. Decision-makers need not only better data on the complexity of factors in environmental and developmental processes affecting human health, but also enhanced understanding of the linkages to be able to know at which level to target their actions. New research tools, methods and paradigms such as geographical information systems, smartphones, and other GPS devices, small sensors to measure environmental exposures, remote sensing and the exposome paradigm together with citizens observatories and science and health impact assessment can now provide this information.

CONCLUSION

While in cities there are often silos of urban planning, mobility and transport, parks and green space, environmental department, (public) health department that do not work together well enough, multi-sectorial approaches are needed to tackle the environmental problems. The city of the future needs to be a green city, a social city, an active city, a healthy city.

Applications of GPS-tracked personal and fixed-location PM(2.5) continuous exposure monitoring

Continued development of personal air pollution monitors is rapidly improving government and research capabilities for data collection. In this study, we tested the feasibility of using GPS-enabled personal exposure monitors to collect personal exposure readings and short-term daily PM2.5 measures at 15 fixed locations throughout a community. The goals were to determine the accuracy of fixed-location monitoring for approximating individual exposures compared to a centralized outdoor air pollution monitor, and to test the utility of two different personal monitors, the RTI MicroPEM V3.2 and TSI SidePak AM510. For personal samples, 24-hr mean PM2.5 concentrations were 6.93 μg/m³ (stderr = 0.15) and 8.47 μg/m³ (stderr = 0.10) for the MicroPEM and SidePak, respectively. Based on time-activity patterns from participant journals, exposures were highest while participants were outdoors (MicroPEM = 7.61 µg/m³, stderr = 1.08, SidePak = 11.85 µg/m³, stderr = 0.83) or in restaurants (MicroPEM = 7.48 µg/m³, stderr = 0.39, SidePak = 24.93 µg/m³, stderr = 0.82), and lowest when participants were exercising indoors (MicroPEM = 4.78 µg/m³, stderr = 0.23, SidePak = 5.63 µg/m³, stderr = 0.08). Mean PM(2.5) at the 15 fixed locations, as measured by the SidePak, ranged from 4.71 µg/m³ (stderr = 0.23) to 12.38 µg/m³ (stderr = 0.45). By comparison, mean 24-h PM(2.5) measured at the centralized outdoor monitor ranged from 2.7 to 6.7 µg/m³ during the study period. The range of average PM(2.5) exposure levels estimated for each participant using the interpolated fixed-location data was 2.83 to 19.26 µg/m³ (mean = 8.3, stderr = 1.4). These estimated levels were compared with average exposure from personal samples. The fixed-location monitoring strategy was useful in identifying high air pollution microclimates throughout the county. For 7 of 10 subjects, the fixed-location monitoring strategy more closely approximated individuals' 24-hr breathing zone exposures than did the centralized outdoor monitor. Highlights are: Individual PM(2.5) exposure levels vary extensively by activity, location and time of day; fixed-location sampling more closely approximated individual exposures than a centralized outdoor monitor; and small, personal exposure monitors provide added utility for individuals, researchers, and public health professionals seeking to more accurately identify air pollution microclimates.

IMPLICATIONS

Personal air pollution monitoring technology is advancing rapidly. Currently, personal monitors are primarily used in research settings, but could they also support government networks of centralized outdoor monitors? In this study, we found differences in performance and practicality for two personal monitors in different monitoring scenarios. We also found that personal monitors used to collect outdoor area samples were effective at finding pollution microclimates, and more closely approximated actual individual exposure than a central monitor. Though more research is needed, there is strong potential that personal exposure monitors can improve existing monitoring networks.

Effect of time-activity adjustment on exposure assessment for traffic-related ultrafine particles

Exposures to ultrafine particles (<100 nm, estimated as particle number concentration, PNC) differ from ambient concentrations because of the spatial and temporal variability of both PNC and people. Our goal was to evaluate the influence of time-activity adjustment on exposure assignment and associations with blood biomarkers for a near-highway population. A regression model based on mobile monitoring and spatial and temporal variables was used to generate hourly ambient residential PNC for a full year for a subset of participants (n=140) in the Community Assessment of Freeway Exposure and Health study. We modified the ambient estimates for each hour using personal estimates of hourly time spent in five micro-environments (inside home, outside home, at work, commuting, other) as well as particle infiltration. Time-activity adjusted (TAA)-PNC values differed from residential ambient annual average (RAA)-PNC, with lower exposures predicted for participants who spent more time away from home. Employment status and distance to highway had a differential effect on TAA-PNC. We found associations of RAA-PNC with high sensitivity C-reactive protein and Interleukin-6, although exposure-response functions were non-monotonic. TAA-PNC associations had larger effect estimates and linear exposure-response functions. Our findings suggest that time-activity adjustment improves exposure assessment for air pollutants that vary greatly in space and time.

Impacts of travel activity and urbanicity on exposures to ambient oxides of nitrogen and on exposure disparities

Daily exposures to ambient oxides of nitrogen were estimated here for residents of Hillsborough County, FL. The 2009 National Household Travel Survey provided geocoded data on fixed activity locations during each person-day sampled. Routes between activity locations were calculated from transportation network data, assuming the quickest travel path. To estimate daily exposure concentrations for each person-day, the exposure locations were matched with diurnally and spatially varying ambient pollutant concentrations derived from CALPUFF dispersion model results. The social distribution of exposures was analyzed by comparing frequency distributions of grouped daily exposure concentrations and by regression modeling. To investigate exposure error, the activity-based exposure estimates were also compared with estimates derived using residence location alone. The mean daily activity-based exposure concentration for the study sample was 17 μg/m³, with values for individual person-day records ranging from 7.0 to 43 μg/m³. The highest mean exposure concentrations were found for the following groups: black (20 μg/m³), below poverty (18 μg/m³), and urban residence location (22 μg/m³). Urban versus rural residence was associated with the largest increase in exposure concentration in the regression (8.3 μg/m³). Time in nonresidential activities, including travel, was associated with an increase of 0.2 μg/m³ per hour. Time spent travelling and at nonresidential locations contributed an average of 6 and 24 %, respectively, to the daily estimate. A mean error of 3.6 %, with range from -64 to 58 %, was found to result from using residence location alone. Exposure error was highest for those who travel most, but lowest for the sociodemographic subgroups with higher mean exposure concentrations (including blacks and those from below poverty households). This work indicates the importance of urbanicity to social disparities in activity-based air pollution exposures. It also suggests that exposure error due to using residence location may be smaller for more exposed groups.

Using personal sensors to assess the exposome and acute health effects

Introduction: The exposome encompasses the totality of human environmental exposures. Recent developments in sensor technology have made it possible to better measure personal exposure to environmental pollutants and other factors. We aimed to discuss and demonstrate the recent developments in personal sensors to measure multiple exposures and possible acute health responses, and discuss the main challenges ahead. Methods: We searched for a range of sensors to measure air pollution, noise, temperature, UV, physical activity, location, blood pressure, heart rate and lung function and to obtain information on green space and emotional status/mood and put it on a person. Results and Conclusions: We discussed the recent developments and main challenges for personal sensors to measure multiple exposures. We found and put together a personal sensor set that measures a comprehensive set of personal exposures continuously over 24 h to assess part of the current exposome and acute health responses. We obtained data for a whole range of exposures and some acute health responses, but many challenges remain to apply the methodology for extended time periods and larger populations including improving the ease of wear, e.g., through miniaturization and extending battery life, and the reduction of costs. However, the technology is moving fast and opportunities will come closer for further wide spread use to assess, at least part of the exposome.