Description and demonstration of the EXPOLIS simulation model: two examples of modeling population exposure to particulate matter

Temporal trend of microenvironmental time-activity patterns of the Seoul population from 2004 to 2022 and its potential impact on exposure assessment

BACKGROUND

Time-activity pattern (TAP) is an important parameter for determining personal exposure to environmental pollutants. Changes in TAPs could have significant implications for the alterations in outcomes of exposure assessments.

OBJECTIVE

This study aimed to evaluate the Seoul population's long-term change in TAPs, along with variations by sociodemographic group.

METHODS

In 2004, 2009, 2014, and 2019, the Time Use Survey of Statistics Korea collected the TAP information of 4036, 2610, 3337, and 2793 Seoul residents, respectively. In 2022, the TAP information of 4401 Seoul residents was collected for Korean Air Pollutant Exposure (KAPEX) research. The microenvironmental TAP changes in the Seoul population from 2004 to 2022 were assessed based on age, gender, work status, and day type.

RESULTS

From 2004 to 2022, Seoul people increasingly spent more time in indoor residences (from 14.8 ± 5.1 h to 15.8 ± 4.5 h) and less time in other indoors (from 7.2 ± 4.5 h to 5.9 ± 4.2 h). Their transit time constantly decreased from 2004 (1.4 ± 1.8 h) to 2022 (1.2 ± 1.3 h), whereas the outdoor time fluctuated throughout the years. From 2004 to 2022, the time of the day spent by Seoul people in residential indoor shifted to later in the morning (2004: 8:30 am; 2022: 9:00 am) and earlier in the evening (2004: 9:30 pm; 2022: 7:00 pm); however, the opposite was true for other indoors (2004: from 8:30 am to 9:30 pm; 2022: from 9:00 am to 7:00 pm) and transits (2004: 7:30-9:30 am and 3:00-8:00 pm; 2022: 7:30-9:00 pm and 5:00-9:00). The time of the day spent in outdoors increased from 2004 to 2019, with a distinct peak observed in 2022 (12:00 pm-2:00 pm). The microenvironmental time trends of adolescents and late-adulthoods differed from those of the other age groups, while those of males differed from females. Also, the microenvironmental time trends of the employed differed from those of the unemployed, and those during weekdays differed from those during weekends.

IMPACT STATEMENT

Microenvironmental TAP should be essentially considered to estimate the actual exposure to pollutants. This study demonstrates the Seoul population's long-term changes in TAP throughout the 18 years as the significant parameter in exposure assessment. Notably, the microenvironmental TAPs of Seoul people shifted, with variations across different sociodemographic groups. Previous studies in Korea did not consider the TAP shifts in exposure assessment; this study highlights the importance of aligning TAP data with concurrent environmental pollutant data and emphasizes the need for refined data collection in future exposure assessments.

A systematic literature review on indoor PM2.5 concentrations and personal exposure in urban residential buildings

Particulate matter with an aerodynamic diameter less than 2.5μm (PM_2.5) is currently a major air pollutant that has been raising public attention. Studies have found that short/long-term exposure to PM_2.5 lead detrimental health effects. Since people in most region of the world spend a large proportion of time in dwellings, personal exposure to PM_2.5 in home microenvironment should be carefully investigated. The objective of this review is to investigate and summary studies in terms of personal exposure to indoor PM_2.5 pollutants from the literature between 2000 and 2021. Factors from both outdoor and indoor environment that have impact on indoor PM_2.5 levels were explicated. Exposure studies were verified relating to individual activity pattern and exposure models. It was found that abundant investigations in terms of personal exposure to indoor PM_2.5 is affected by factors including concentration level, exposure duration and personal diversity. Personal exposure models, including microenvironment model, mathematical model, stochastic model and other simulation models of particle deposition in different regions of human airway are reviewed. Further studies joining indoor measurement and simulation of PM_2.5 concentration and estimation of deposition in human respiratory tract are necessary for individual health protection.

A stochastic exposure model integrating random forest and agent-based approaches: Evaluation for PM2.5 in Jiangsu, China

This research proposes an Activity Pattern embedded Air Pollution Exposure Model (AP2EM), based on survey data of when, where, and how people spend their time and indoor/outdoor ratios for microenvironments. AP2EM integrates random forest and agent-based approaches to simulate the stochastic exposure to outdoor fine particulate matter (PM_2.5) along with indoor and in-vehicle PM_2.5 of outdoor origin. The R² of the linear regression between the model's calculations and personal measurement was 0.65, which was more accurate than the commonly-used aggregated exposure (AE) model and the outdoor exposure (OE) model. The population-weighted PM_2.5 exposure estimated by the AP2EM was 36.7 μg/m³ in Jiangsu, China, during 2014-2017. The OE model overestimated exposure by 54.0%, and the AE model underestimated exposure by 6.5%. These misestimate reflect ignorance of traditional studies on effects posed from time spent indoors (~85%) and doing low respiratory rate activities (~93%), problems of biased sampling, and neglecting low probability events. The proposed AP2EM treats activity patterns of individuals as chains and uses stochastic estimates to model activity choices, providing a more comprehensive understanding of human activity and exposure characteristics. Overall, the AP2EM is applicable for other air pollutants in different regions and benefits China's air pollution control policy designs.

Environmental Health Surveillance System for a Population Using Advanced Exposure Assessment

Human exposure to air pollution is a major public health concern. Environmental policymakers have been implementing various strategies to reduce exposure, including the 10th-day-no-driving system. To assess exposure of an entire population of a community in a highly polluted area, pollutant concentrations in microenvironments and population time–activity patterns are required. To date, population exposure to air pollutants has been assessed using air monitoring data from fixed atmospheric monitoring stations, atmospheric dispersion modeling, or spatial interpolation techniques for pollutant concentrations. This is coupled with census data, administrative registers, and data on the patterns of the time-based activities at the individual scale. Recent technologies such as sensors, the Internet of Things (IoT), communications technology, and artificial intelligence enable the accurate evaluation of air pollution exposure for a population in an environmental health context. In this study, the latest trends in published papers on the assessment of population exposure to air pollution were reviewed. Subsequently, this study proposes a methodology that will enable policymakers to develop an environmental health surveillance system that evaluates the distribution of air pollution exposure for a population within a target area and establish countermeasures based on advanced exposure assessment.

Methodology for Estimating the Lifelong Exposure to PM2.5 and NO2—The Application to European Population Subgroups

Health impacts of air pollutants, especially fine particles (PM2.5) and NO2, have been documented worldwide by epidemiological studies. Most of the existing studies utilised the concentration measured at the ambient stations to represent the pollutant inhaled by individuals. However, these measurement data are in fact not able to reflect the real concentration a person is exposed to since people spend most of their time indoors and are also affected by indoor sources. The authors developed a probabilistic methodology framework to simulate the lifelong exposure to PM2.5 and NO2 simultaneously for population subgroups that are characterised by a number of indicators such as age, gender and socio-economic status. The methodology framework incorporates the methods for simulating the long-term outdoor air quality, the pollutant concentration in different micro-environments, the time-activity pattern of population subgroups and the retrospective life course trajectories. This approach was applied to the population in the EU27 countries plus Norway and Switzerland and validated with the measurement data from European multi-centre study, EXPOLIS. Results show that the annual average exposure to PM2.5 and NO2 at European level kept increasing from the 1950s to a peak between the 1980s and the 1990s and showed a decrease until 2015 due to the implementation of a series of directives. It is also revealed that the exposure to both pollutants was affected by geographical location, gender and income level. The average annual exposure over the lifetime of an 80-year-old European to PM2.5 and NO2 amounted to 23.86 (95% CI: 2.95–81.86) and 13.49 (95% CI: 1.36–43.84) µg/m3. The application of this methodology provides valuable insights and novel tools for exposure modelling and environmental studies.

Characterization of a High PM2.5 Exposure Group in Seoul Using the Korea Simulation Exposure Model for PM2.5 (KoSEM-PM) Based on Time⁻Activity Patterns and Microenvironmental Measurements

The Korea Simulation Exposure Model for fine particulate matter (PM_2.5) (KoSEM-PM) was developed to estimate population PM_2.5 exposure in Korea. The data were acquired based on 59,945 min of the actual microenvironmental PM_2.5 measurements and on the time–activity patterns of 8072 residents of Seoul. The aims of the study were to estimate daily PM_2.5 exposure of Seoul population, and to determine the characteristics of a high exposure group. KoSEM-PM estimated population exposures by applying the PM_2.5 distribution to the matching time–activity patterns at 10-min intervals. The mean personal PM_2.5 exposure level of the surveyed subjects in Seoul was 26.0 ± 2.7 µg/m³ (range: 21.0–40.2 µg/m³) in summer. Factors significantly associated with high exposure included day of the week, age, industry sector, job type, and working hours. Individuals surveyed on Saturdays were more likely to be in the high exposure group than those surveyed on weekdays and Sundays. Younger, non-office-working individuals with longer working hours were more likely to be in the high exposure group. KoSEM-PM could be a useful tool to estimate population exposure levels to other region in Korea; to expand its use, microenvironmental measurements are required for other region in Korea.

A multipollutant evaluation of APEX using microenvironmental ozone, carbon monoxide, and particulate matter (PM2.5) concentrations measured in Los Angeles by the exposure classification project

This paper describes an operational evaluation of the US Environmental Protection Agency's (EPA) Air Pollution Exposure Model (APEX). APEX simulations for a multipollutant ambient air mixture, i.e. ozone (O₃), carbon monoxide (CO), and particulate matter 2.5 microns in diameter or less (PM_2.5), were performed for two seasons in three study areas in central Los Angeles. APEX predicted microenvironmental concentrations were compared with concentrations of these three pollutants monitored in the Exposure Classification Project (ECP) study during the same periods. The ECP was designed expressly for evaluating exposure models and measured concentrations inside and outside 40 microenvironments. This evaluation study identifies important uncertainties in APEX inputs and model predictions useful for guiding further exposure model input data and algorithm development efforts. This paper also presents summaries of the concentrations in the different microenvironments.

Seasonal Differences in Determinants of Time Location Patterns in an Urban Population: A Large Population-Based Study in Korea

Time location patterns are a significant factor for exposure assessment models of air pollutants. Factors associated with time location patterns in urban populations are typically due to high air pollution levels in urban areas. The objective of this study was to determine the seasonal differences in time location patterns in two urban cities. A Time Use Survey of Korean Statistics (KOSTAT) was conducted in the summer, fall, and winter of 2014. Time location data from Seoul and Busan were collected, together with demographic information obtained by diaries and questionnaires. Determinants of the time spent at each location were analyzed by multiple linear regression and the stepwise method. Seoul and Busan participants had similar time location profiles over the three seasons. The time spent at own home, other locations, workplace/school and during walk were similar over the three seasons in both the Seoul and Busan participants. The most significant time location pattern factors were employment status, age, gender, monthly income, and spouse. Season affected the time spent at the workplace/school and other locations in the Seoul participants, but not in the Busan participants. The seasons affected each time location pattern of the urban population slightly differently, but overall there were few differences.

PM2.5 Population Exposure in New Delhi Using a Probabilistic Simulation Framework

This paper presents a Geographical Information System (GIS) based probabilistic simulation framework to estimate PM2.5 population exposure in New Delhi, India. The framework integrates PM2.5 output from spatiotemporal LUR models and trip distribution data using a Gravity model based on zonal data for population, employment and enrollment in educational institutions. Time-activity patterns were derived from a survey of randomly sampled individuals (n = 1012) and in-vehicle exposure was estimated using microenvironmental monitoring data based on field measurements. We simulated population exposure for three different scenarios to capture stay-at-home populations (Scenario 1), working population exposed to near-road concentrations during commutes (Scenario 2), and the working population exposed to on-road concentrations during commutes (Scenario 3). Simulated annual average levels of PM2.5 exposure across the entire city were very high, and particularly severe in the winter months: ∼200 μg m(-3) in November, roughly four times higher compared to the lower levels in the monsoon season. Mean annual exposures ranged from 109 μg m(-3) (IQR: 97-120 μg m(-3)) for Scenario 1, to 121 μg m(-3) (IQR: 110-131 μg m(-3)), and 125 μg m(-3) (IQR: 114-136 μ gm(-3)) for Scenarios 2 and 3 respectively. Ignoring the effects of mobility causes the average annual PM2.5 population exposure to be underestimated by only 11%.

Modeling and estimating manganese concentrations in rural households in the mining district of Molango, Mexico

Airborne manganese (Mn) is considered the most hazardous route of exposure since Mn particles can enter into the body through the lung and may access the brain directly through olfactory uptake, thereby bypassing homeostatic excretory mechanisms. Environmental indoor and outdoor manganese concentrations in PM2.5 were monitored in ten rural households from two communities of Hidalgo, Mexico, from 2006 to 2007. Indoor and outdoor air samples of PM2.5 were collected using MiniVol samplers, and Mn concentrations in the filters were measured using proton-induced X-ray emission (PIXE). An adjusted generalized linear mixed model was applied and then used for estimating indoor concentrations in non-monitored households. Our monitoring results showed a higher daily average concentration of indoor PM2.5 vs. outdoor PM2.5 (46.4 vs. 36.2 μg/m(3), respectively); however, manganese concentration in PM2.5 indoor and outdoor was 0.09 μg/m(3) in both sceneries. Predictor variables of indoor Mn concentration were outdoor Mn concentration (64.5% increase per 0.1 μg/m(3) change in Mn) and keeping the windows open (4.2% increase). Using these predictors, the average estimated indoor Mn concentration in PM2.5 was 0.07 μg/m(3) (SD = 0.05). Our results confirm the direct effect of outdoor Mn levels, opening house windows, and the distance to the mining chimney in indoor Mn levels in houses.

The microenvironmental modelling approach to assess children's exposure to air pollution - A review

Exposures to a wide spectrum of air pollutants were associated to several effects on children's health. Exposure assessment can be used to establish where and how air pollutants' exposures occur. However, a realistic estimation of children's exposures to air pollution is usually a great ethics challenge, especially for young children, because they cannot intentionally be exposed to contaminants and according to Helsinki declaration, they are not old enough to make a decision on their participation. Additionally, using adult surrogates introduces bias, since time-space-activity patterns are different from those of children. From all the different available approaches for exposure assessment, the microenvironmental (ME) modelling (indirect approach, where personal exposures are estimated or predicted from microenvironment measurements combined with time-activity data) seemed to be the best to assess children's exposure to air pollution as it takes into account the varying levels of pollution to which an individual is exposed during the course of the day, it is faster and less expensive. Thus, this review aimed to explore the use of the ME modelling approach methodology to assess children's exposure to air pollution. To meet this goal, a total of 152 articles, published since 2002, were identified and titles and abstracts were scanned for relevance. After exclusions, 26 articles were fully reviewed and main characteristics were detailed, namely: (i) study design and outcomes, including location, study population, calendar time, pollutants analysed and purpose; and (ii) data collection, including time-activity patterns (methods of collection, record time and key elements) and pollution measurements (microenvironments, methods of collection and duration and time resolution). The reviewed studies were from different parts of the world, confirming the worldwide application, and mostly cross-sectional. Longitudinal studies were also found enhancing the applicability of this approach. The application of this methodology on children is different from that on adults because of data collection, namely the methods used for collecting time-activity patterns must be different and the time-activity patterns are itself different, which leads to select different microenvironments to the data collection of pollutants' concentrations. The most used methods to gather information on time-activity patterns were questionnaires and diaries, and the main microenvironments considered were home and school (indoors and outdoors). Although the ME modelling approach in studies to assess children's exposure to air pollution is highly encouraged, a validation process is needed, due to the uncertainties associated with the application of this approach.

Modeling population exposure to ultrafine particles in a major Italian urban area

Average daily ultrafine particles (UFP) exposure of adult Milan subpopulations (defined on the basis of gender, and then for age, employment or educational status), in different exposure scenarios (typical working day in summer and winter) were simulated using a microenvironmental stochastic simulation model. The basic concept of this kind of model is that time-weighted average exposure is defined as the sum of partial microenvironmental exposures, which are determined by the product of UFP concentration and time spent in each microenvironment. In this work, environmental concentrations were derived from previous experimental studies that were based on microenvironmental measurements in the city of Milan by means of personal or individual monitoring, while time-activity patterns were derived from the EXPOLIS study. A significant difference was observed between the exposures experienced in winter (W: 28,415 pt/cm3) and summer (S: 19,558 pt/cm3). Furthermore, simulations showed a moderate difference between the total exposures experienced by women (S: 19,363 pt/cm3; W: 27,623 pt/cm3) and men (S: 18,806 pt/cm3; W: 27,897 pt/cm3). In addition, differences were found as a function of (I) age, (II) employment status and (III) educational level; accordingly, the highest total exposures resulted for (I) 55-59 years old people, (II) housewives and students and (III) people with higher educational level (more than 10 years of scholarity). Finally, significant differences were found between microenvironment-specific exposures.

Modelling of human exposure to air pollution in the urban environment: a GPS-based approach

The main objective of this work was the development of a new modelling tool for quantification of human exposure to traffic-related air pollution within distinct microenvironments by using a novel approach for trajectory analysis of the individuals. For this purpose, mobile phones with Global Positioning System technology have been used to collect daily trajectories of the individuals with higher temporal resolution and a trajectory data mining, and geo-spatial analysis algorithm was developed and implemented within a Geographical Information System to obtain time-activity patterns. These data were combined with air pollutant concentrations estimated for several microenvironments. In addition to outdoor, pollutant concentrations in distinct indoor microenvironments are characterised using a probabilistic approach. An example of the application for PM2.5 is presented and discussed. The results obtained for daily average individual exposure correspond to a mean value of 10.6 and 6.0-16.4 μg m(-3) in terms of 5th-95th percentiles. Analysis of the results shows that the use of point air quality measurements for exposure assessment will not explain the intra- and inter-variability of individuals' exposure levels. The methodology developed and implemented in this work provides time-sequence of the exposure events thus making possible association of the exposure with the individual activities and delivers main statistics on individual's air pollution exposure with high spatio-temporal resolution.

Analysis of intervention strategies for inhalation exposure to polycyclic aromatic hydrocarbons and associated lung cancer risk based on a Monte Carlo population exposure assessment model

It is difficult to evaluate and compare interventions for reducing exposure to air pollutants, including polycyclic aromatic hydrocarbons (PAHs), a widely found air pollutant in both indoor and outdoor air. This study presents the first application of the Monte Carlo population exposure assessment model to quantify the effects of different intervention strategies on inhalation exposure to PAHs and the associated lung cancer risk. The method was applied to the population in Beijing, China, in the year 2006. Several intervention strategies were designed and studied, including atmospheric cleaning, smoking prohibition indoors, use of clean fuel for cooking, enhancing ventilation while cooking and use of indoor cleaners. Their performances were quantified by population attributable fraction (PAF) and potential impact fraction (PIF) of lung cancer risk, and the changes in indoor PAH concentrations and annual inhalation doses were also calculated and compared. The results showed that atmospheric cleaning and use of indoor cleaners were the two most effective interventions. The sensitivity analysis showed that several input parameters had major influence on the modeled PAH inhalation exposure and the rankings of different interventions. The ranking was reasonably robust for the remaining majority of parameters. The method itself can be extended to other pollutants and in different places. It enables the quantitative comparison of different intervention strategies and would benefit intervention design and relevant policy making.

Pedestrian exposure to size-resolved particles in Milan

Measurement campaigns for airborne particles along a pedestrian route in the city center of Milan were performed by means of a portable instrument consisting of an optical particle counter and a global positioning system (GPS) signal receiver. Based on the size-resolved particle number concentration data and on proper density factors experimentally determined for Milan urban area, the mass concentrations were calculated in terms of particulate matter with aerodynamic diameters < or =10 microm (PM10), < or =2.5 pm (PM2.5), and < or =1 microm (PM1). Besides directly measuring the personal exposure to PM throughout the route, the measurement campaigns pointed out small spatial and temporal variations of the concentration ranges in the different urban microenvironments visited along the route as well as very peculiar features of the particles levels in the underground subway. These findings suggested that the personal exposure of pedestrians in the city center could be estimated by simply taking into account the exposure at the open air and in the subway. The comparison between measured and calculated exposures according to the microenvironment-based estimation results in reasonable accordance, even though the estimations tend to slightly underestimate (12%) the actual measured exposure.

Modelling inhalation exposure to combustion-related air pollutants in residential buildings: Application to health impact assessment

Buildings in developed countries are becoming increasingly airtight as a response to stricter energy efficiency requirements. At the same time, changes are occurring to the ways in which household energy is supplied, distributed and used. These changes are having important impacts on exposure to indoor air pollutants in residential buildings and present new challenges for professionals interested in assessing the effects of housing on public health. In many circumstances, models are the most appropriate way with which to examine the potential outcomes of future environmental and/or building interventions and policies. As such, there is a need to consider the current state of indoor air pollution exposure modelling. Various indoor exposure modelling techniques are available, ranging from simple statistical regression and mass-balance approaches, to more complex multizone and computational fluid dynamics tools that have correspondingly large input data requirements. This review demonstrates that there remain challenges which limit the applicability of current models to health impact assessment. However, these issues also present opportunities for better integration of indoor exposure modelling and epidemiology in the future. The final part of the review describes the application of indoor exposure models to health impact assessments, given current knowledge and data, and makes recommendations aimed at improving model predictions in the future.

Contribution to volatile organic compound exposures from time spent in stores and restaurants and bars

Many people spend time in stores and restaurants, yet there has been little investigation of the influence of these microenvironments on personal exposure. Relative to the outdoors, transportation, and the home, these microenvironments have high concentrations of several volatile organic compounds (VOCs). We developed a stochastic model to examine the effect of VOC concentrations in these microenvironments on total personal exposure for (1) non-smoking adults working in offices who spend time in stores and restaurants or bars and (2) non-smoking adults who work in these establishments. We also compared the effect of working in a smoking versus non-smoking restaurant or bar. Input concentrations for each microenvironment were developed from the literature whereas time activity inputs were taken from the National Human Activity Patterns Survey. Time-averaged exposures were simulated for 5000 individuals over a weeklong period for each analysis. Mean contributions to personal exposure from non-working time spent in stores and restaurants or bars range from <5% to 20%, depending on the VOC and time-activity patterns. At the 95th percentile of the distribution of the proportion of personal exposure attributable to time spent in stores and restaurants or bars, these microenvironments can be responsible for over half of a person's total exposure to certain VOCs. People working in restaurants or bars where smoking is allowed had the highest fraction of exposure attributable to their workplace. At the median, people who worked in stores or restaurants tended to have 20-60% of their total exposures from time spent at work. These results indicate that stores and restaurants can be large contributors to personal exposure to VOCs for both workers in those establishments and for a subset of people who visit these places, and that incorporation of these non-residential microenvironments can improve models of personal exposure distributions.

Personal PM10 exposure in asthmatic adults in Padova, Italy: seasonal variability and factors affecting individual concentrations of particulate matter

Personal exposure to PM(10) measured in different seasons in a sample of asthmatic subjects living in Padova (Northern Italy) was compared with simultaneously measured outdoor PM(10) concentrations. The specific contribution of ambient PM(10) and other factors to individual exposure was evaluated in one of the areas of Europe with the worst air pollution. Thirty-one asthmatic subjects (21 non-smokers and 10 smokers) carried personal PM(10) monitors for six 24-hr sessions, in different seasons of the year. Concomitant daily 24-hr ambient PM(10) concentrations were measured by air quality monitoring networks. A multivariate analysis was performed to identify factors explaining personal exposure to PM(10), using a random effect model. The analysis on the 31 subjects referred to a total of 155 observations. The mean personal PM(10) exposure was higher (range 79.3-126.1microg/m(3)) than the outdoor concentrations (range 37.3-85.4microg/m(3)) in all seasons; and personal exposures varied less than outdoor PM(10) levels from one season to another. Smokers had significantly higher personal PM(10) concentrations than non-smokers (127.99 vs 78.8microg/m(3); T=-5.70; p<0.001). Moderate correlations emerged between outdoor and personal PM(10) concentrations. The correlation improved after excluding subjects exposed to active or passive smoking (median Pearson's R 0.41 vs 0.26). Considering all the subjects, smoking was the main factor affecting personal exposure, contributing to 41% of the variability. Outdoor PM(10) concentrations (25%), temperature (12%) and season (15%) also contributed to personal PM(10) exposure. Outdoor PM(10) (46%), temperature (20%), season (19%) and time spent indoors (6%) were significantly associated with personal exposure in non-smokers. We concluded that it is crucial to perform personal monitoring and to evaluate the complexity of factors that contribute to individual PM exposure. While tobacco smoke was the primary source of PM(10) in all subjects, the contribution of ambient components was particularly relevant for the personal exposure levels of our non-smokers living in a highly-polluted environment.

Spatial variations in estimated chronic exposure to traffic-related air pollution in working populations: a simulation

Background

Chronic exposure to traffic-related air pollution is associated with a variety of health impacts in adults and recent studies show that exposure varies spatially, with some residents in a community more exposed than others. A spatial exposure simulation model (SESM) which incorporates six microenvironments (home indoor, work indoor, other indoor, outdoor, in-vehicle to work and in-vehicle other) is described and used to explore spatial variability in estimates of exposure to traffic-related nitrogen dioxide (not including indoor sources) for working people. The study models spatial variability in estimated exposure aggregated at the census tracts level for 382 census tracts in the Greater Vancouver Regional District of British Columbia, Canada. Summary statistics relating to the distributions of the estimated exposures are compared visually through mapping. Observed variations are explored through analyses of model inputs.

Results

Two sources of spatial variability in exposure to traffic-related nitrogen dioxide were identified. Median estimates of total exposure ranged from 8 μg/m³ to 35 μg/m³ of annual average hourly NO₂ for workers in different census tracts in the study area. Exposure estimates are highest where ambient pollution levels are highest. This reflects the regional gradient of pollution in the study area and the relatively high percentage of time spent at home locations. However, for workers within the same census tract, variations were observed in the partial exposure estimates associated with time spent outside the residential census tract. Simulation modeling shows that some workers may have exposures 1.3 times higher than other workers residing in the same census tract because of time spent away from the residential census tract, and that time spent in work census tracts contributes most to the differences in exposure. Exposure estimates associated with the activity of commuting by vehicle to work were negligible, based on the relatively short amount of time spent in this microenvironment compared to other locations. We recognize that this may not be the case for pollutants other than NO_2. These results represent the first time spatially disaggregated variations in exposure to traffic-related air pollution within a community have been estimated and reported.

Conclusion

The results suggest that while time spent in the home indoor microenvironment contributes most to between-census tract variation in estimates of annual average exposures to traffic-related NO₂, time spent in the work indoor microenvironment contributes most to within-census tract variation, and time spent in transit by vehicle makes a negligible contribution. The SESM has potential as a policy evaluation tool, given input data that reflect changes in pollution levels or work flow patterns due to traffic demand management and land use development policy.

Characterisation of urban inhalation exposures to benzene, formaldehyde and acetaldehyde in the European Union: comparison of measured and modelled exposure data

BACKGROUND, AIM AND SCOPE

All across Europe, people live and work in indoor environments. On average, people spend around 90% of their time indoors (homes, workplaces, cars and public transport means, etc.) and are exposed to a complex mixture of pollutants at concentration levels that are often several times higher than outdoors. These pollutants are emitted by different sources indoors and outdoors and include volatile organic compounds (VOCs), carbonyls (aldehydes and ketones) and other chemical substances often adsorbed on particles. Moreover, legal obligations opposed by legislations, such as the European Union's General Product Safety Directive (GPSD) and Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH), increasingly require detailed understanding of where and how chemical substances are used throughout their life-cycle and require better characterisation of their emissions and exposure. This information is essential to be able to control emissions from sources aiming at a reduction of adverse health effects. Scientifically sound human risk assessment procedures based on qualitative and quantitative human exposure information allows a better characterisation of population exposures to chemical substances. In this context, the current paper compares inhalation exposures to three health-based EU priority substances, i.e. benzene, formaldehyde and acetaldehyde.

MATERIALS AND METHODS

Distributions of urban population inhalation exposures, indoor and outdoor concentrations were created on the basis of measured AIRMEX data in 12 European cities and compared to results from existing European population exposure studies published within the scientific literature. By pooling all EU city personal exposure, indoor and outdoor concentration means, representative EU city cumulative frequency distributions were created. Population exposures were modelled with a microenvironment model using the time spent and concentrations in four microenvironments, i.e. indoors at home and at work, outdoors at work and in transit, as input parameters. Pooled EU city inhalation exposures were compared to modelled population exposures. The contributions of these microenvironments to the total daily inhalation exposure of formaldehyde, benzene and acetaldehyde were estimated. Inhalation exposures were compared to the EU annual ambient benzene air quality guideline (5 microg/m3-to be met by 2010) and the recommended (based on the INDEX project) 30-min average formaldehyde limit value (30 microg/m3).

RESULTS

Indoor inhalation exposure contributions are much higher compared to the outdoor or in-transit microenvironment contributions, accounting for almost 99% in the case of formaldehyde. The highest in-transit exposure contribution was found for benzene; 29.4% of the total inhalation exposure contribution. Comparing the pooled AIRMEX EU city inhalation exposures with the modelled exposures, benzene, formaldehyde and acetaldehyde exposures are 5.1, 17.3 and 11.8 microg/m3 vs. 5.1, 20.1 and 10.2 microg/m3, respectively. Together with the fact that a dominating fraction of time is spent indoors (>90%), the total inhalation exposure is mostly driven by the time spent indoors.

DISCUSSION

The approach used in this paper faced three challenges concerning exposure and time-activity data, comparability and scarce or missing in-transit data inducing careful interpretation of the results. The results obtained by AIRMEX underline that many European urban populations are still exposed to elevated levels of benzene and formaldehyde in the inhaled air. It is still likely that the annual ambient benzene air quality guideline of 5 microg/m3 in the EU and recommended formaldehyde 30-min average limit value of 30 microg/m3 are exceeded by a substantial part of populations living in urban areas. Considering multimedia and multi-pathway exposure to acetaldehyde, the biggest exposure contribution was found to be related to dietary behaviour rather than to inhalation.

CONCLUSIONS

In the present study, inhalation exposures of urban populations were assessed on the basis of novel and existing exposure data. The indoor residential microenvironment contributed most to the total daily urban population inhalation exposure. The results presented in this paper suggest that a significant part of the populations living in European cities exceed the annual ambient benzene air quality guideline of 5 microg/m3 in the EU and recommended (INDEX project) formaldehyde 30-min average limit value of 30 microg/m3.

RECOMMENDATIONS AND PERSPECTIVES

To reduce exposures and consequent health effects, adequate measures must be taken to diminish emissions from sources such as materials and products that especially emit benzene and formaldehyde in indoor air. In parallel, measures can be taken aiming at reducing the outdoor pollution contribution indoors. Besides emission reduction, mechanisms to effectively monitor and manage the indoor air quality should be established. These mechanisms could be developed by setting up appropriate EU indoor air guidelines.

Parameter and model uncertainty in a life-table model for fine particles (PM2.5): a statistical modeling study

BACKGROUND

The estimation of health impacts involves often uncertain input variables and assumptions which have to be incorporated into the model structure. These uncertainties may have significant effects on the results obtained with model, and, thus, on decision making. Fine particles (PM2.5) are believed to cause major health impacts, and, consequently, uncertainties in their health impact assessment have clear relevance to policy-making. We studied the effects of various uncertain input variables by building a life-table model for fine particles.

METHODS

Life-expectancy of the Helsinki metropolitan area population and the change in life-expectancy due to fine particle exposures were predicted using a life-table model. A number of parameter and model uncertainties were estimated. Sensitivity analysis for input variables was performed by calculating rank-order correlations between input and output variables. The studied model uncertainties were (i) plausibility of mortality outcomes and (ii) lag, and parameter uncertainties (iii) exposure-response coefficients for different mortality outcomes, and (iv) exposure estimates for different age groups. The monetary value of the years-of-life-lost and the relative importance of the uncertainties related to monetary valuation were predicted to compare the relative importance of the monetary valuation on the health effect uncertainties.

RESULTS

The magnitude of the health effects costs depended mostly on discount rate, exposure-response coefficient, and plausibility of the cardiopulmonary mortality. Other mortality outcomes (lung cancer, other non-accidental and infant mortality) and lag had only minor impact on the output. The results highlight the importance of the uncertainties associated with cardiopulmonary mortality in the fine particle impact assessment when compared with other uncertainties.

CONCLUSION

When estimating life-expectancy, the estimates used for cardiopulmonary exposure-response coefficient, discount rate, and plausibility require careful assessment, while complicated lag estimates can be omitted without this having any major effect on the results.

Variability of total exposure to PM2.5 related to indoor and outdoor pollution sources Krakow study in pregnant women

The study is a part of an ongoing prospective cohort study on the relationship between the exposure to environmental factors during pregnancy and birth outcomes and health of newborns. We have measured personal PM(2.5) level in the group of 407 non-smoking pregnant women during the 2nd trimester of pregnancy. On average, the participants from the city center were exposed to higher exposure than those from the outer city area (GM=42.0 microg/m(3), 95% CI: 36.8-48.0 vs. 35.8 microg/m(3), 95% CI: 33.5-38.2 microg/m(3)). More than 20% of study subjects were affected by high level of PM(2.5) pollution (above 65 microg/m(3)). PM(2.5) concentrations were higher during the heating season (GM=43.4 microg/m(3), 95% CI: 40.1-46.9 microg/m(3)) compared to non-heating season (GM=29.8 microg/m(3), 95% CI: 27.5-32.2 microg/m(3)). Out of all potential outdoor air pollution sources (high traffic density, bus depot, waste incinerator, industry etc.) considered in the bivariate analysis, only the proximity of industrial plant showed significant impact on the personal exposure (GM=54.3 microg/m(3), 95% CI: 39.4-74.8 microg/m(3)) compared with corresponding figure for those who did not declare living near the industrial premises (GM=36.2 microg/m(3), 95% CI: 34.1-38.4 microg/m(3)). The subjects declaring high exposure to ETS (>10 cigarettes daily) have shown very high level of personal exposure (GM=88.8 microg/m(3), 95% CI: 73.9-106.7 microg/m(3)) compared with lower ETS exposure (< or =10 cigarettes) (GM=46.3 microg/m(3), 95% CI: 40.0-53.5 microg/m(3)) and no-ETS exposure group (GM=33.9 microg/m(3), 95% CI: 31.8-36.1 microg/m(3)). The contribution of the background ambient PM(10) level was very strong determinant of the total personal exposure to PM(2.5) and it explained about 31% of variance between the subjects followed by environmental tobacco smoke (10%), home heating by coal/wood stoves (2%), other types of heating (2%) and the industrial plant localization in the proximity of household (1%).

Reduction potential of urban PM2.5 mortality risk using modern ventilation systems in buildings

Urban PM2.5 (particulate matter with aerodynamic diameter smaller than 2.5 microm) is associated with excess mortality and other health effects. Stationary sources are regulated and considerable effort is being put into developing low-pollution vehicles and environment-friendly transportation systems. While waiting for technological breakthroughs in emission controls, the current work assesses the exposure reductions achievable by a complementary means: efficient filtration of supply air in buildings. For this purpose infiltration factors for buildings of different ages are quantified using Exposures of Adult Urban Populations in Europe Study (EXPOLIS) measurements of indoor and outdoor concentrations in a population-based probability sample of residential and occupational buildings in Helsinki, Finland. These are entered as inputs into an evaluated simulation model to compare exposures in the current scenario with an alternative scenario, where the distribution of ambient PM2.5 infiltration factors in all residential and occupational buildings are assumed to be similar to the subset of existing occupational buildings using supply air filters. In the alternative scenario exposures to ambient PM2.5 were reduced by 27%. Compared with source controls, a significant additional benefit is that infiltration affects particles from all outdoor sources. The large fraction of time spent indoors makes the reduction larger than what probably can be achieved by local transport policies or other emission controls in the near future.

PRACTICAL IMPLICATIONS

It has been suggested that indoor concentrations of ambient particles and the associated health risks can be reduced by using mechanical ventilation systems with supply air filtering in buildings. The current work quantifies the effects of these concentration reductions on population exposures using population-based data from Helsinki and an exposure model. The estimated exposure reductions suggest that correctly defined building codes may reduce annual premature mortality by hundreds in Finland and by tens of thousands in the developed world altogether.

The EXPOLIS study: implications for exposure research and environmental policy in Europe

Exposure analysis is a crucial part of effective management of public health risks caused by pollutants and chemicals in our environment. During the last decades, more data required for exposure analysis has become available, but the need for direct population based measurements of exposures is still clear. The current work (i) describes the European EXPOLIS study, designed to produce this kind of exposure data for major air pollutants in Europe, and the database created to make the collected data available for researchers (ii) reviews the exposure analysis conducted and results published so far using these data and (iii) discusses the implications of the results from the point of view of research and environmental policy in Europe. Fine particle (with 37 elements and black smoke), nitrogen dioxide, volatile organic compounds (30 compounds) and carbon monoxide inhalation exposures and exposure-related questionnaire data were measured in seven European cities during 1996-2000. The EXPOLIS database has been used for exposure analysis of these pollutants for 4 years now and results have been published in approximately 30 peer-reviewed journal papers, demonstrating the versatility, usability and scientific value of such a data set. The multipollutant exposure data from the same subjects in the random population samples allows for analyses of the determinants, microenvironments and sources of exposures to multipollutant mixtures and associations between the different air pollutants. This information is necessary and useful for developing effective policies and control strategies for healthier environment.

Simulation of working population exposures to carbon monoxide using EXPOLIS-Milan microenvironment concentration and time-activity data

Current air pollution levels have been shown to affect human health. Probabilistic modeling can be used to assess exposure distributions in selected target populations. Modeling can and should be used to compare exposures in alternative future scenarios to guide society development. Such models, however, must first be validated using existing data for a past situation. This study applied probabilistic modeling to carbon monoxide (CO) exposures using EXPOLIS-Milan data. In the current work, the model performance was evaluated by comparing modeled exposure distributions to observed ones. Model performance was studied in detail in two dimensions; (i) for different averaging times (1, 8 and 24 h) and (ii) using different detail in defining the microenvironments in the model (two, five and 11 microenvironments). (iii) The number of exposure events leading to exceeding the 8-h guideline was estimated. Population time activity was modeled using a fractions-of-time approach assuming that some time is spent in each microenvironment used in the model. This approach is best suited for averaging times from 24 h upwards. In this study, we tested how this approach affects results when used for shorter averaging times, 1 and 8 h. Models for each averaging time were run with two, five and 11 microenvironments. The two-microenvironment models underestimated the means and standard deviations (SDs) slightly for all averaging times. The five- and 11-microenvironment models matched the means quite well but underestimated SDs in several cases. For 1- and 24-h averaging times the simulated SDs are slightly smaller than the corresponding observed values. The 8-h model matched the observed exposure levels best. The results show that for CO (i) the modeling approach can be applied for averaging times from 8 to 24 h and as a screening model even to an averaging time of 1 h; (ii) the number of microenvironments affects only weakly the results and in the studied cases only exposure levels below the 80th percentile; (iii) this kind of model can be used to estimate the number of high-exposure events related to adverse health effects. By extrapolation beyond the observed data, it was shown that Milanese office workers may experience adverse health effects caused by CO.

EXPOLIS simulation model: PM2.5 application and comparison with measurements in Helsinki

PM(2.5) exposure distributions of adult Helsinki citizens were simulated using a probabilistic simulation framework. Simulation results were compared to corresponding personal exposure distributions measured in the EXPOLIS study in Helsinki. The simpler models 1 and 2 (with two and three microenvironments, respectively) predict the general outline of the exposure distributions reasonably well. Compared to the observed exposure distribution, the mean is underestimated by less than 3 microg m(-3) (20%) and the standard deviation by 23-35%. In the improved simulation models (3 and 4), the environmental tobacco smoke (ETS)-exposed subjects are excluded, the time-activity models of working and nonworking subpopulations are modeled separately, and the correlations of input concentration and time fraction variables have been accounted for. The output of these models was very close to the observed distributions; the differences in the means were less than 0.1 microg m(-3) and the differences in standard deviation less than 1%. We conclude that when the required input data are available or can be reliably estimated, the target population PM(2.5) exposure distributions can be predicted accurately enough for most practical purposes using this kind of a microenvironment model.