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Matti Jantunen

Publications and source records attributed to Matti Jantunen.

6 recordsLinked to original sources

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 PM(2.5) example uses microenvironment measurement and time-activity data from the EXPOLIS study to model PM(2.5) population exposure distributions in four European cities. The results are compared to the observed personal exposure distributions from the same study. The Dutch PM(10) 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.

Air Pollutants↗

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.

Adult↗

Comparison of black smoke and PM2.5 levels in indoor and outdoor environments of four European cities.

Recent studies on separated particle-size fractions highlight the health significance of particulate matter smaller than 2.5 microm (PM2.5), but gravimetric methods do not identify specific particle sources. Diesel exhaust particles (DEP) contain elemental carbon (EC), the dominant light-absorbing substance in the atmosphere. Black smoke (BS) is a measure for light absorption of PM and, thus, an alternative way to estimating EC concentrations, which may serve as a proxy for diesel exhaust emissions. We analyzed PM2.5 and BS data collected within the EXPOLIS study (Air Pollution Exposure Distribution within Adult Urban Populations in Europe) in Athens, Basel, Helsinki, and Prague. 186 indoor/outdoor filter pairs were sampled and analyzed. PM2.5 and BS levels were lowest in Helsinki, moderate in Basel, and remarkably higher in Athens and Prague. In each city, Spearman correlation coefficients of indoor versus outdoor were higher for BS (range rspearman: 0.57-0.86) than for PM2.5 (0.05-0.69). In a BS linear regression model (all data), outdoor levels explained clearly more of indoor variation (86%) than in the corresponding PM2.5 model (59%). In conclusion, ambient BS seizes a health-relevant fraction of fine particles to which people are exposed indoors and outdoors and exposure to which can be assessed by monitoring outdoor concentrations. BS measured on PM2.5 filters can be recommended as a valid and cheap additional indicator in studies on combustion-related air pollution and health.

Air Pollutants↗

Chapter one: exposure measurements.

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

Air Pollutants↗

Why particles?

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Air Pollutants↗

Photometrically measured continuous personal PM(2.5) exposure: levels and correlation to a gravimetric method.

There is evidence that hourly variations in exposure to airborne particulate matter (PM) may be associated with adverse health effects. Still there are only few published data on short-term levels of personal exposure to PM in community settings. The objectives of the study were to assess hourly and shorter-term variations in personal PM(2.5) exposure in Helsinki, Finland, and to compare results from portable photometers to simultaneously measured gravimetric concentrations. The effect of relative humidity on the photometric results was also evaluated. Personal PM(2.5) exposures of elderly persons were assessed for 24 h every second week, resulting in 308 successful measurements from 47 different subjects. Large changes in concentrations in minutes after cooking or changing microenvironment were seen. The median of daily 1-h maxima was over twice the median of 24-h averages. There was a strong significant association between the two means, which was not linear. Median (95th percentile) of the photometric 24-h concentrations was 12.1 (37.7) and of the 24-h gravimetric concentrations 9.2 (21.3) microg/m3. The correlation between the photometric and the gravimetric method was quite good (R2=0.86). Participants spent 94.1% of their time indoors or in a vehicle, where relative humidity is usually low and thus not likely to cause significant effects on photometric results. Even outdoors, the relative humidity had only modest effect on concentrations. Photometers are a promising method to explore the health effects of short-term variation in personal PM(2.5) exposure.

Activities of Daily Living↗