Tarja Koskentalo

Associations of fine and ultrafine particulate air pollution with stroke mortality in an area of low air pollution levels

Background and Purpose— Daily variation in outdoor concentrations of inhalable particles (PM10 <10 &mgr;m in diameter) has been associated with fatal and nonfatal stroke. Toxicological and epidemiological studies suggest that smaller, combustion-related particles are especially harmful. We therefore evaluated the effects of several particle measures including, for the first time to our knowledge, ultrafine particles (<0.1 &mgr;m) on stroke. Methods— Levels of particulate and gaseous air pollution were measured in 1998 to 2004 at central outdoor monitoring sites in Helsinki. Associations between daily levels of air pollutants and deaths caused by stroke among persons aged 65 years or older were evaluated in warm and cold seasons using Poisson regression. Results— There was a total of 1304 and 1961 deaths from stroke in warm and cold seasons, respectively. During the warm season, there were positive associations of stroke mortality with current- and previous-day levels of fine particles (<2.5 &mgr;m, PM2.5) (6.9%; 95% CI, 0.8% to 13.8%; and 7.4%; 95% CI, 1.3% to 13.8% for an interquartile increase in PM2.5) and previous-day levels of ultrafine particles (8.5%; 95% CI, −1.2% to 19.1%) and carbon monoxide (8.3; 95% CI, 0.6 to 16.6). Associations for fine particles were mostly independent of other pollutants. There were no associations in the cold season. Conclusions— Our results suggest that especially PM2.5, but also ultrafine particles and carbon monoxide, are associated with increased risk of fatal stroke, but only during the warm season. The effect of season might be attributable to seasonal differences in exposure or air pollution mixture.

A modelling system for predicting urban air pollution: model description and applications in the Helsinki metropolitan area

Abstract We have developed a modelling system for evaluating the traffic volumes, emissions from stationary and vehicular sources, and atmospheric dispersion of pollution in an urban area. The dispersion modelling is based on combined application of the urban dispersion modelling system (UDM-FMI) and the road network dispersion model (CAR-FMI). The system includes also a meteorological pre-processing model and a statistical and graphical analysis of the computed time series of concentrations. The modelling system contains a method, which allows for the chemical interaction of pollutants, originating from a large number of urban sources. This paper presents an overview of the modelling system and its application for estimating the NOx and NO2 concentrations in the Helsinki metropolitan area in 1993. A companion paper addresses comparison of model predictions with the results of an urban measurement network. This modelling system is an important regulatory assessment tool for the national environmental authorities.

A model for evaluating the population exposure to ambient air pollution in an urban area

A mathematical model is presented for the determination of human exposure to ambient air pollution in an urban area. The main objective was to evaluate the spatial and temporal variation of average exposure of the urban population to ambient air pollution in different microenvironments with reasonable accuracy, instead of analysing in detail personal exposures for specific individuals. We have utilised a previously developed modelling system for predicting the traffic flows and emissions, emissions originating from stationary sources, and atmospheric dispersion of pollution in an urban area. A model was developed for combining the predicted concentrations, information on peoples activities (such as the time spent at home, in the workplace and at other places of activity during the day) and location of the population. Time-microenvironment activity data for the working-age population was obtained from the EXPOLIS study (air pollution distributions within adult urban populations in Europe). Information on the location of homes and workplaces was obtained from local municipalities. The concentrations of NO2 were modelled over the Helsinki Metropolitan Area for 1996 and 1997. The computed results were processed and visualised using the geographical information system (GIS) MapInfo. The utilisation of the modelling system has been illustrated by presenting numerical results for the Helsinki Metropolitan Area. The results show the spatial and temporal (diurnal) variation of the ambient air NO2 concentrations, the population density and the corresponding average exposure. The model developed has been designed to be utilised by municipal authorities in urban planning, e.g., for evaluating the impacts of traffic planning and land use scenarios.

A modelling system for predicting urban air pollution: comparison of model predictions with the data of an urban measurement network in Helsinki

We have developed a modelling system for predicting the tra

A measurement campaign in a street canyon in Helsinki and comparison of results with predictions of the OSPM model

c volumes, emissions from stationary and vehicular sources, and atmospheric dispersion of pollution in an urban area. A companion paper addresses model development and its applications. This paper describes a comparison of the predicted NO x and NO 2 concentrations with the results of an urban air quality monitoring network. We performed a statistical analysis concerning the agreement of the predicted and measured hourly time series of concentrations, at four monitoring stations in the Helsinki metropolitan area in 1993. The predicted and measured NO 2 concentrations agreed well at all the stations considered. The agreement of model predictions and measurements for NO x and NO 2 was better for the two suburban monitoring stations, compared with the two urban stations, located in downtown Helsinki. ( 2000 Elsevier Science Ltd. All rights reserved.

Evaluation of the OSPM model combined with an urban background model against the data measured in 1997 in Runeberg Street, Helsinki

In 1997, a measuring campaign was conducted in a street canyon (Runeberg St.) in Helsinki. Hourly mean concentrations of CO, NOx, NO2 and O3 were measured at street and roof levels, the latter in order to determine the urban background concentrations. The relevant hourly meteorological parameters were measured at roof level; these included wind speed and direction, temperature and solar radiation. Hourly street level measurements and on-site electronic traffic counts were conducted throughout the whole of 1997; roof level measurements were conducted for approximately two months, from 3 March to 30 April in 1997. CO and NOx emissions from traffic were computed using measured hourly traffic volumes and evaluated emission factors. The Operational Street Pollution Model (OSPM) was used to calculate the street concentrations and the results were compared with the measurements. The overall agreement between measured and predicted concentrations was good for CO and NOx (fractional bias were −4.2 and +4.5%, respectively), but the model overpredicted the measured NO2 concentrations (fractional bias was +22%). The agreement between the measured and predicted values was also analysed in terms of its dependence on wind speed and direction; the latter analysis was performed separately for two categories of wind velocity. The model qualitatively reproduces the observed behaviour very well. The database, which contains all measured and predicted data, is available for further testing of other street canyon dispersion models. The dataset contains a larger proportion of low wind speed cases, compared with other available street canyon measurement datasets.

Building an Environmental Information System for Personalized Content Delivery

Abstract In 1997, a measuring campaign was conducted in a street canyon (Runeberg Street) in Helsinki. Hourly street level measurements and on-site electronic traffic counts were conducted throughout the whole of 1997; roof level measurements were conducted for approximately two months during the so-called intensive measuring campaign, from 3 March to 30 April 1997. Hourly mean concentrations of NOx, NO2, O3 and CO were measured at street and roof levels; the relevant hourly meteorological parameters were measured at roof level. We present here an evaluation of the Operational Street Pollution Model (OSPM) street canyon dispersion model against the data measured during the whole of 1997. As the roof level concentrations and meteorological measurements were not available for the whole year, we utilised computed or meteorologically pre-processed values. The use of modelled urban background concentrations and meteorological values (instead of on-site roof level measurements) did not lessen the agreement between modelled and measured average concentration values at street level. The agreement between the temporal variations of predictions and measured data was also fairly good; for instance, the corresponding index of agreement values for NOx, NO2 and CO were 0.89, 0.81 and 0.87, respectively. However, as expected, the agreement in the temporal variations was somewhat better using actual measured on-site data during the intensive measuring campaign, than when using modelled urban background concentrations and meteorological values. This study demonstrates that it is possible to utilise the street canyon dispersion model OSPM with reasonable accuracy using modelled urban background and pre-processed meteorological values as model input.

Personalized Environmental Service Orchestration for Quality of Life Improvement

Citizens are increasingly aware of the influence of environmental and meteorological conditions on the quality of their life. This results in an increasing demand for personalized environmental information, i.e., information that is tailored to citizens’ specific context and background. In this work we describe the development of an environmental information system that addresses this demand in its full complexity. Specifically, we aim at developing a system that supports submission of user generated queries related to environmental conditions. From the technical point of view, the system is tuned to discover reliable data in the web and to process these data in order to convert them into knowledge, which is stored in a dedicated repository. At run time, this information is transferred into an ontology-structured knowledge base, from which then information relevant to the specific user is deduced and communicated in the language of their preference.

Personalized Environmental Service Configuration and Delivery Orchestration: The PESCaDO Demonstrator

Environmental and meteorological conditions are of utmost importance for the population, as they are strongly related to the quality of life. Citizens are increasingly aware of this importance. This awareness results in an increasing demand for environmental information tailored to their specific needs and background. We present an environmental information platform that supports submission of user queries related to environmental conditions and orchestrates results from complementary services to generate personalized suggestions. From the technical viewpoint, the system discovers and processes reliable data in the web in order to convert them into knowledge. At run time, this information is transferred into an ontology-structured knowledge base, from which then information relevant to the specific user is deduced and communicated in the language of their preference. The platform is demonstrated with real world use cases in the south area of Finland showing the impact it can have on the quality of everyday life.

Development and Verification of a Modelling System for Predicting Urban NO2 Concentrations

Citizens are increasingly aware of the influence of environmental and meteorological conditions on the quality of their life. This results in an increasing demand for personalized environmental information, i.e., information that is tailored to citizens’ specific context and background. In this demonstration, we present an environmental information system that addresses this demand in its full complexity in the context of the PESCaDO EU project. Specifically, we will show a system that supports submission of user generated queries related to environmental conditions. From the technical point of view, the system is tuned to discover reliable data in the web and to process these data in order to convert them into knowledge, which is stored in a dedicated repository. At run time, this information is transferred into an ontology-based knowledge base, from which then information relevant to the specific user is deduced and communicated in the language of their preference.