Otto Hänninen

Indoor aerosols: from personal exposure to risk assessment

Motivated by growing considerations of the scale, severity, and risks associated with human exposure to indoor particulate matter, this work reviewed existing literature to: (i) identify state-of-the-art experimental techniques used for personal exposure assessment; (ii) compare exposure levels reported for domestic/school settings in different countries (excluding exposure to environmental tobacco smoke and particulate matter from biomass cooking in developing countries); (iii) assess the contribution of outdoor background vs indoor sources to personal exposure; and (iv) examine scientific understanding of the risks posed by personal exposure to indoor aerosols. Limited studies assessing integrated daily residential exposure to just one particle size fraction, ultrafine particles, show that the contribution of indoor sources ranged from 19% to 76%. This indicates a strong dependence on resident activities, source events and site specificity, and highlights the importance of indoor sources for total personal exposure. Further, it was assessed that 10-30% of the total burden of disease from particulate matter exposure was due to indoor-generated particles, signifying that indoor environments are likely to be a dominant environmental factor affecting human health. However, due to challenges associated with conducting epidemiological assessments, the role of indoor-generated particles has not been fully acknowledged, and improved exposure/risk assessment methods are still needed, together with a serious focus on exposure control.

Environmental burden of disease in Europe : assessing nine risk factors in six countries

Background: Environmental health effects vary considerably with regard to their severity, type of disease, and duration. Integrated measures of population health, such as environmental burden of disease (EBD), are useful for setting priorities in environmental health policies and research. This review is a summary of the full Environmental Burden of Disease in European countries (EBoDE) project report. Objectives: The EBoDE project was set up to provide assessments for nine environmental risk factors relevant in selected European countries (Belgium, Finland, France, Germany, Italy, and the Netherlands). Methods: Disability-adjusted life years (DALYs) were estimated for benzene, dioxins, secondhand smoke, formaldehyde, lead, traffic noise, ozone, particulate matter (PM2.5), and radon, using primarily World Health Organization data on burden of disease, (inter)national exposure data, and epidemiological or toxicological risk estimates. Results are presented here without discounting or age-weighting. Results: About 3–7% of the annual burden of disease in the participating countries is associated with the included environmental risk factors. Airborne particulate matter (diameter ≤ 2.5 μm; PM2.5) is the leading risk factor associated with 6,000–10,000 DALYs/year and 1 million people. Secondhand smoke, traffic noise (including road, rail, and air traffic noise), and radon had overlapping estimate ranges (600–1,200 DALYs/million people). Some of the EBD estimates, especially for dioxins and formaldehyde, contain substantial uncertainties that could be only partly quantified. However, overall ranking of the estimates seems relatively robust. Conclusions: With current methods and data, environmental burden of disease estimates support meaningful policy evaluation and resource allocation, including identification of susceptible groups and targets for efficient exposure reduction. International exposure monitoring standards would enhance data quality and improve comparability. Citation: Hänninen O, Knol AB, Jantunen M, Lim TA, Conrad A, Rappolder M, Carrer P, Fanetti AC, Kim R, Buekers J, Torfs R, Iavarone I, Classen T, Hornberg C, Mekel OC, EBoDE Working Group. 2014. Environmental burden of disease in Europe: assessing nine risk factors in six countries. Environ Health Perspect 122:439–446; http://dx.doi.org/10.1289/ehp.1206154

Fine particle (PM2.5) measurement methodology, quality assurance procedures, and pilot results of the EXPOLIS study.

EXPOLIS is a European multicenter (Athens, Basel, Grenoble, Helsinki, Milan, and Prague) air pollution exposure study. It is the first international, population-based, large-scale study, where personal exposures to PM2 5 aerosol particles (together with volatile organic compounds and carbon monoxide) are being monitored. EXPOLIS is performed in six different centers across Europe, the sampled aerosol concentrations vary greatly, and the mi-croenvironmental samples are not collected with the same equipment as the personal samples. Therefore careful equipment selection, methods development and testing, and thorough quality assurance and quality control (QA & QC) procedures are essential for producing reliable and comparable PM2.5 data. This paper introduces the equipment, the laboratory test results, the pilot results, the standard operating procedures, and the QA & QC procedures of EXPOLIS. Test results show good comparability and repeatability between personal and microenvironmen-tal monitors for PM2.5 at different concentration levels measured across Europe in EXPOLIS centers.

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

As a part of the EXPOLIS study, a stochastic exposure-modeling framework was developed. The framework is useful to compare exposure distributions of different (sub-) populations or different scenarios, and to gain insight into population exposure distributions and exposure determinants. It was implemented in an MS-Excel workbook using @Risk add-on software. Basic concept of the framework is that time-weighted average exposure is a sum of partial exposures in the visited microenvironments. Partial exposure is determined by the concentration and the time spent in the microenvironment. In the absence of data, indoor concentrations are derived as a function of ambient concentrations, effective penetration rates and contribution of indoor sources. Framework input parameters are described by probability distributions. A lognormal distribution is assumed for the microenvironment concentrations and for the contribution of indoor sources, and a beta distribution for the time spent in a microenvironment and for the penetration factor. Mean and standard deviation values parameterize the distributions. In this paper, Latin Hypercube sampling is used for the input distributions. The outcome of the framework is an estimate of the population exposure distribution for the selected air pollutant. The framework is best suited for averaging times from 24 h upwards. Sensitivity analyses can be performed to determine the most influential factors of exposure. The application of the framework is illustrated in two examples. The EXPOLIS PM2.5 example uses microenvironment measurement and time–activity data from the EXPOLIS study to model PM2.5 population exposure distributions in four European cities. The results are compared to the observed personal exposure distributions from the same study. The Dutch PM10 example uses input data from several (Dutch) databases and from literature, and shows a more complex application of the framework for comparison of scenarios and subpopulations.

Population exposure to fine particles and estimated excess mortality in Finland from an East European wildfire episode

Long-range transported particulate matter (PM) air pollution episodes associated with wildfires in the Eastern Europe are relatively common in Southern and Southeastern Finland. In severe cases such as in August–September 2002, the reduced visibility and smell of the smoke, and symptoms such as irritation of eyes and airways experienced by the population raise the issue into the headlines. Because PM air pollution, in general, has been identified as a major health risk, and the exposures are of repeating nature, the issue warrants a risk assessment to estimate the magnitude of the problem. The current work uses the available air quality data in Finland to estimate population exposures caused by one of the worst episodes experienced in this decade. This episode originated from wildfires in Russia, Belarus, Ukraine, and the Baltic countries. The populations of 11 Southern Finnish provinces were exposed between 26 August and 8 September 2002, for 2 weeks to an additional population-weighted average PM2.5 level of 15.7 μg/m3. Assuming similar effect on mortality for these particles as observed in epidemiological time series studies on urban particles (0.5%–2% increase in mortality per 10 μg/m3, central estimate 1%), this exposure level would be associated with 9–34 cases (17 cases central estimate) of additional mortality. Epidemiological evidence specific to particles from biomass combustion is scarce, affecting also the reliability of the current risk assessment. Do the wildfire aerosols exhibit the same level of toxicity as the urban particles? To shed light on this question, it is interesting to look at the exposure data in relationship to the observed daily mortality in Finland, even though the limited duration of the episode allows only for a weak statistical power. The percentage increases observed (0.8%–2.1% per 10 μg/m3 of fine PM) are in line with the more general estimates for urban PM and those used in the current risk assessment.

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.

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

PM2.5 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 μg 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 μg 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 PM2.5 exposure distributions can be predicted accurately enough for most practical purposes using this kind of a microenvironment model.

Health Effects Caused by Primary Fine Particulate Matter (PM2.5) Emitted from Buses in the Helsinki Metropolitan Area, Finland

Fine particle (PM(2.5)) emissions from traffic have been associated with premature mortality. The current work compares PM(2.5)-induced mortality in alternative public bus transportation strategies as being considered by the Helsinki Metropolitan Area Council, Finland. The current bus fleet and transportation volume is compared to four alternative hypothetical bus fleet strategies for the year 2020: (1) the current bus fleet for 2020 traffic volume, (2) modern diesel buses without particle traps, (3) diesel buses with particle traps, and (4) buses using natural gas engines. The average population PM(2.5) exposure level attributable to the bus emissions was determined for the 1996-1997 situation using PM(2.5) exposure measurements including elemental composition from the EXPOLIS-Helsinki study and similar element-based source apportionment of ambient PM(2.5) concentrations observed in the ULTRA study. Average population exposure to particles originating from the bus traffic in the year 2020 is assumed to be proportional to the bus emissions in each strategy. Associated mortality was calculated using dose-response relationships from two large cohort studies on PM(2.5) mortality from the United States. Estimated number of deaths per year (90% confidence intervals in parenthesis) associated with primary PM(2.5) emissions from buses in Helsinki Metropolitan Area in 2020 were 18 (0-55), 9 (0-27), 4 (0-14), and 3 (0-8) for the strategies 1-4, respectively. The relative differences in the associated mortalities for the alternative strategies are substantial, but the number of deaths in the lowest alternative, the gas buses, is only marginally lower than what would be achieved by diesel engines equipped with particle trap technology. The dose-response relationship and the emission factors were identified as the main sources of uncertainty in the model.

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.

Personal carbon monoxide exposure levels: contribution of local sources to exposures and microenvironment concentrations in Milan

In the framework of the EXPOLIS study in Milan, Italy, 48-h carbon monoxide (CO) exposures of 50 office workers were monitored over a 1-year period. In this work, the exposures were assessed for different averaging times and were compared with simultaneous ambient fixed-site concentrations. The effect of gas cooking and smoking and different methods of commuting on the microenvironment and exposure levels of CO were investigated. During the sampling the subjects completed a time–microenvironment–activity diary differentiating 11 microenvironments and three exposure influencing activities: gas cooking, smoking and commuting. After sampling, all exposure and time allocation data were stored in a relational database that is used in data analyses. Ambient 48-h and maximum 8-h distributions were similar compared to the respective personal exposures. The maximum 1-h personal exposures were much higher than the maximum 8-h exposures. The maximum 1-h exposures were as well higher than the corresponding ambient distribution. These findings indicate that high short-term exposures were not reflected in ambient monitoring data nor by long-term exposures. When gas cooking or smoking was present, the indoor levels at “home-” and in “other indoor” microenvironments were higher than without their presence. Compared with ambient data, the latter source was the most affective to increase the indoor levels. Exposure during commuting was higher than in all other microenvironments; the highest daily exposure contribution was found during “car/taxi” driving. Most of the CO exposure is acquired in indoor microenvironments. For the indoor microenvironments, ambient CO was the weakest predictor for “home indoor” concentrations, where the subjects spent most of their time, and the strongest for “other indoor” concentrations, where the smallest fraction of the time was spent. Of the main indoor sources, gas cooking, on average, significantly raised the indoor exposure concentrations for 45 min and tobacco smoking for 30 min. The highest exposure levels were experienced in street commuting. Personal exposures were well predicted, but 1-h maximum personal exposures were poorly predicted, by respective ambient air quality data. By the use of time–activity diaries, ETS exposure at the workplaces were probably misclassified due to differences in awareness to tobacco smoke between smokers and nonsmokers.